dataesr/scientific-paragraphs-categorization huggingface dataset:
It may look innocuous. A er all, the Python documentation, states that "Python uses the Mersenne Twister as the core generator. It produces 53-bit precision floats and has a period of 2**19937-1". Someone trying to replicate the work however might choose to use the RNG from the NumPy library. e NumPy library is extensively used in the science community, and it provides an implementation of the Mersene Twister generator too. Unfortunately, the way the seed is interpreted by the two implementations is different, yielding different random sequences. Here we are able to replicate exactly 5  the behavior of the pure-Python random walker by se ing the internal state of the NumPy RNG appropriately, but only because we have access to specific technical details (the use of the random module of the standard Python library of CPython 3.
Conclusion roughout the evolution of a small random walk example implemented in Python, we illustrated some of the issues that may plague scientific code. e code may be correct and of good quality, but still many problems may reduce its contribution to scientific knowledge. To make these problems explicit, we articulated five characteristics that a code should possess to be a useful part of a scientific publication: it should be re-runnable, repeatable, reproducible, reusable and replicable. Running old code on tomorrow's computer and so ware stacks may not be possible. But recreating the old code's execution environment may be: to ensure that the long-term re-runnability of a code, its execution environment must be documented. For our example, a single comment went a long way to transform the R 0 code into the R 1 (re-runnable) one.
str(datetime. datetime. utcnow()), # Copyright (c) 2017 Nicolas P. Rougier and Fabien C. Y. Benureau # Release under the BSD 2-clause license # Tested with CPython 3. 6. 2 / macOS 10. 12. 6 / 64 bits architecture import sys, subprocess, datetime, random L 4: Re-runnable, repeatable, reproducible random walk (R 3 ) raw code, archive def generate_walk(): x = 0 walk = [] for i in range(10): if random. uniform(-1, +1) > 0: x += 1 else: x -= 1 walk. append(x) return walk # If repository is dirty, don t run anything if subprocess. call(( git , diff-index , --quiet , HEAD )): print( Repository␣is␣dirty,␣please␣commit␣first ) sys. exit(1) # Get git hash if any revision = subprocess. check_output(( git , rev-parse , HEAD )) # Unit test random. seed(42) assert generate_walk() == [1, 0, -1, -2, -1, 0, 1, 0, -1, -2] # Random walk for 10 steps seed = 1 random.
This model is made available in an OWL ontology  [40]. A new ontology is developed to define and quantify energy-related occupant behaviour in buildings  [41]. It improves interoperability between occupant behavior models and building energy modeling programs  [42]. Corry et al. [43]  proposed an ontology to aid the integration of soft AEC data into the existing linked dataspace for the evaluation of building performance. A hybrid structure was designed to link time-series related building performance and contextual data silos in linked dataspaces  [27]. Dibley et al. [44]  proposed an ontology framework, which enables realtime building performance monitoring based on semantic building data. The building data is transformed using a building ontology relying on IFC and the Ontosensor ontology.
3. Datum Streams which are expressions of informational concepts, for example temperature and relative humidity found in AEC data silos. As mentioned a priori, operators are categorised into two forms, regular arithmetic and special. The former category includes the normal operators (addition, subtraction, multiplication and division). The later category includes derivatives of the sum and average operators that are peculiar to building performance calculations. Rather than express lower and upper bounds of summation, the sum operator operates over the child-parent relationships expressed within linked semantic data; for instance, the sum of the heating energy consumption of a floor is the sum of the consumption for all rooms on the floor. The operation is completed thus; the list of spaces on a floor is identified, and heating consumption for each of these is calculated before being summed to provide a result for the floor.
Sensors associated with each relevant building object are then identified; this permits linking to relevant time-series data retained in relational database tables. Having identified relevant tables, queries are generated to retrieve this data. A) The time period and building object associated with the performance metric are extracted (step 1). The time period is retained as a parameter to constrain the data streams. The performance formulae is also extracted and parsed to identify its arguments (step 2); these arguments identity what data streams must be prepared as input to the performance formulae. B) The building object, extracted in step 1, serves -as described a priori -as the starting point for mining the linked space to discover sensors whose data can serve as data streams (steps 3 and 4).
Sensor information is stored in an SSN-RDF file based on the SSN ontology  [56]. A DB-RDF file represents schema information of databases that store time-series data related to building performance based on MySQL  [27]. the linked data layer leverages SPARQL and SQL queries to finish linking of cross-domain silos in the source layer. SPARQL queries are generated using the definition of ontologies, includinghttps://www. sharelatex. com/project/57f60b57834d86bf759469 ifcOWL ontology, SSN ontology, and the proposed ontology. SQL queries are generated with database schema information. The Apache Jena framework and Java database connectivity (JDBC) application programming interface (API) are used to execute SPARQL and SQL queries. the support services layer is intended to simplify the consumption of linked dataspaces by encapsulating common services for reuse.
In the context of this work, two new components, the querying and computing module, were required to implement the contributions developed in this paper. The querying module parses requirements in selected performance metrics and automatically generate SPARQL and SQL queries to obtain data streams. The computing module transfers performance metrics to normal mathematical formulae and leverages computer algebra systems to calculate these formulae for assessment results. Services contained in this layer use code developed in Java within the Eclipse IDE. the interface layer locates at the top of the architecture and displays the framework as the graphical user interfaces (GUI) in Windows systems. It visualises and enriches the information coming from the support services layer. Actionable information is now available to the building manger in a manner that was previously unavailable.
The SQL query combines sensor information and a fixed time period to extract sensing records from relational databases. Listing 1: The SPARQL query extracting sensor entities related to the performance metric. SELECT ?sensor WHERE{ OPTIONAL{ ?device rdf:type ifc:IfcEnergyConversionDevice. ?device rdf:type ifc:IfcAirToAirHeatRecovery. } ?device ifc:IfcRelConnects ?object. ?object rdf:type ifc:IfcZone. ?object ifc:Name "Pool Hall". ?device ssn:observedBy ?sensor. ?sensor rdf:type ssn:Sensor.
The ontology can quantify impact of metrics such as building function, thermal load and system performance, as well as energy consumption. The framework has the potential to work well in buildings with data that is structured in easily accessible digital formats, e. g. BIM and data in relational databases. The flexibility and modularity of the ontology structure allows the description of solutions for buildings in different climate zones and geographical locations. Thirdly, there are a number of boundaries to this approach. The evaluation of performance metrics requires datum streams from cross-domain data silos. For the majority of buildings only subsets of performance metrics will be calculable due to the absence of sensor data, e. g. temperature sensors in every zone. Installation of additional sensors may also be required in older buildings in order to obtain specific building information (e.
This paper presents a hybrid pedestrian detection system on the basis of 3D LIDAR data and visible images of the same scene. The proposed method consists of two main stages. In the first stage, the 3D LIDAR data are classified to obtain a set of clusters, which will be mapped into the visible image to get regions of interests (ROIs). The second stage classifies the ROIs (pedestrian/non pedestrian) using SVM as classifier and color based histogram of oriented gradients (HOG) together with the local self-similarity (LSS) as features. The proposed method has been tested on LIPD dataset and the results demonstrate its effectiveness.
• Complex backgrounds: the pedestrians must be well detected in outdoor urban scenarios despite the wide range of illumination and weather conditions that affect the quality of the sensed information. • Occlusions and different scales between pedestrians as well as the appearance of the pedestrians at different viewing angles. In this paper, we present a new pedestrian detection approach that includes both LIDAR and vision sensors. The proposed approach is an extension of that early presented in  (Lahmyed and El Ansari, 2016)  by the same authors. It takes as input a 3D LI-DAR data and its corresponding visible image. The proposed approach is composed of two major stages. The first stage clusters the 3D LIDAR data into connected components using the DBSCAN  (Ester et al. , 1996)  technique.
PROPOSED METHOD The proposed algorithm takes as input a set a 3D LI-DAR data and one visible image of the same scene. The 3D LIDAR data is used to extract ROIs while the visible image image is used to classify each of the extracted ROIs as pedestrian or non pedestrian. The proposed method is achieved in two main steps: (1) ROIs extraction based on the LIDAR data and ROIs classification based on the information issued from the visible image. Here, we detail each of the two steps.
The decision function is given by: f (x) = sgn(x. w T + b). ( 3 ) Figure  2 : The margin between the two datasets. In this paper, we have utilized not only a linear function but we used a Gaussian kernel as well, as follows: K(x i , x j ) = e - x i -x j 2 2σ 2 (4) and the decision function for a new input vector is : f (x) = sgn( N s ∑ i=1 α i y i K(s i , x) + b). (5).
Dataset The Laser and Image Pedestrian Detection Dataset (LIPD)  (Premebida and Nunes, 2016)  has been used to test the performance of the proposed system. The LIPD Dataset was collected in outdoor conditions (the Coimbra Univeristy/ISR Campus zone). It was recorded using Yamaha-ISR electric vehicle (IS-RobotCar), equipped with a multilayer automotive laserscanner Alasca-XT from Ibeo, a TopCon Hyper-Pro GPS device in RTK mode, one IMU Mti from Xsens and a monocular Guppy camera. The LIPD dataset contains 4823 frames (one frame is composed of one visible image and its corresponding LIDAR data). The images format is 24-bit color jpg and their size is 640 x 480 pixels.
Parameters Setting The parameters involved in the various modules of the proposed system were selected empirically using images from LIPD dataset. In the first stage, two parameters of DBSCAN clustering algorithm were utilized: EPS and the minimum number of points needed to compose a dense region (MinPts). shows the computing time, while varying these two parameters over 900 images chosen from the LIPD dataset. Notice that a correct detected person is counted true positive if its corresponding bounding box overlaps with at least 70 % of the area covered by the person present in the image. We observe that the number of true positives is not quite sensitive to EPS parameter. However, the computing time increments with these two parameters. EPS = 5, and MinPts = 400 are chosen since they guarantee high accuracy (more than 890 true positives) while the computational time is at its lowest value (less than 40 ms).
To get the optimal parameters of the classifiers utilized in the system, cross-validation experiments have been achieved on a training dataset. The images of the dataset are divided into a training and validation subsets. By training and testing the classifiers on the two subsets using various parameters settings, the parameters that enable to get the high classification accuracy have been selected. The classifiers have been retrained again using the chosen parameters on the LIPD training dataset. In this work, we use SVM with radial basis function (RBF) kernel with C=2 and G=0. 03. The classification accuracy varies while changing the value of these two parameters and become higher as these parameters reach the values 2 and 0. 03, respectively, for all the features used. 1. The required time for the whole detection method is 39.
In order to justify the utilization of the SVM with radial basis function (RBF) kernel as classifier, a comparison with SVM linear and Adaboost classifiers is made. Table  4  shows the comparison results in terms of F-measure and consuming time. We notice that the combination HOG-Color and LSS outperforms all the other features. It achieves 95. 15%, 94. 51% and 92. 61% using SVM-K , SVM-L and Adaboost classifiers, respectively. The Adaboost classifier gives the best consuming time (only 47. 08 ms), however, the  Figure  6  shows the Receiver Operating Characteristic curve obtained by the new system for pedestrian detection. It attains 95. 86% AUC for LIPD dataset on average run time of 8-10 frames per second.
Performance Comparison with State of the Arts A comparison versus some state-of-the-art works is given to evaluate the performance of the proposed method. Such a comparison has been conducted with the methods illustrated in  (Gao et al. , 2015; Premebida et al. , 2009a; Premebida et al. , 2009b)  using the LIPD dataset. The classification accuracies achieved by the earlier methods as well as the proposed method are presented in the Table  5. The method illustrated in  (Premebida et al. , 2009b)  presents a system to detect pedestrians using the trainable fusion method FGMM as combination of Gaussian Mixture Model (GMM) and Fisher's Linear Discriminated Analysis classifiers (FLDA). A layered graph model in D-depth domains and RGB-image are used in  (Gao et al. , 2015)  to detect and track the pedestrians.
Method CCR (%) LIDAR-GMMC/vision-FLDA  (Premebida et al. , 2009b)  89. 92 LGM  (Gao et al. , 2015)  87. 70 MCI-NN  (Premebida et al. , 2009a)  93. 10 The new method 95. 86 Figures 8 and 9 illustrate some other examples of detection results while using the proposed method to images of LIPD dataset. In Figure  8 , the pedestrians included in the four images have been successfully detected. In Figure  9 , the pedestrians could not be detected due to different reasons. The pedestrians were too far which make the LIDAR sensor incapable to measure properly their distances. Consequently, their corresponding ROIs won't extracted.
CONCLUSION AND PERSPECTIVES In this paper, a two steps pedestrian detection system is presented. In the first step, we generate ROIs based on clustering the data provided by a LIDAR sensor. Next, we project the obtained clusters on the corresponding visible image. In the second step, we combine the so-called HOG-Color with LSS feature to build a new descriptor. This descriptor is then used with the SVM classifier to detect pedestrians from the ROIs. Experimental results illustrate that the proposed system achieves AUC of 95. 86% on the LIPD dataset. Although the proposed method proves its effectiveness compared to recent state of-art ones, we are planning in the future to extend it by using the 3D LI-DAR data not only to generate the ROIs but to improve classification step as well. Furthermore, we aim to ameliorate the system by employing other machine learning techniques to accelerate the classification process.
MATERIALS AND METHODS Study design, sampling, genetic analyses, and pharmacokinetic analyses Data from a previous study in which we investigated the influence of variation in the gene encoding CES1 on the PK variables of MPH in 44 healthy volunteers,  29  were made available to the present study. In brief, the 44 volunteers, consisting of 19 men and 25 women aged 20-29 years and with a body mass index ranging from 18-28, had been selected from a total of 200 recruited subjects on the basis of CES1 genotyping and classified into six CES1 genotype groups, which included the CES1 wildtype genotype and five genotypes of CES1 with presumed or documented effect on drug metabolism. These six groups were designated 1-6 and consisted of group 1: wild type with 2 copies of CES1 and without nonsynonymous SNPs, n 5 16; group 2: carriers of four CES1 copies including two CES1A2 copies, n 5 5; group 3: carriers of the 143E allele (rs71647871), n 5 6; group 4: carriers of three CES1 copies including one CES1A2 copy with overlapping Sp1 sites conferring increased transcriptional activity, n 5 2; group 5: carriers of the CES1A1c variant, n 5 4; and group 6: carriers of three copies of CES1 including one CES1A2 copy, which carries the common and low-activity promoter, n 5 10.
Bile acid profiling Bile acid extraction was performed by adding methanol containing internal standards to 50 lL plasma. Samples were analyzed using an ACQUITY UPLC system with an HSS T3 column coupled to a 6530 Accurate Mass QToF (Agilent Technologies). Following liquid chromatography-mass spectrometry analysis, data was preprocessed by performing peak integration, background correction, and determination of the relative ratios between metabolites and their corresponding internal standards. The metabolite units were expressed as the peak area ratios of the target analyte to the respective internal standard. An in-house written tool was applied using the QC samples to compensate for shifts in the sensitivity of the mass spectrometer throughout the batches. Both internal standard correction and QC correction were applied to the dataset before reporting results.
A retrospective power analysis was conducted on the dataset in Stage et al. 29  2017 showing that a minimum of 6 subjects per group, more precisely 5. 06 subjects, was required to detect a difference between groups. Therefore, genotype comparisons were restricted to only those groups that had at least >5 subjects, namely groups 1 (wild type) and groups 3 and 6. Multiple testing correction for each set of comparisons was performed using the Benjamini & Hochberg correction method to control for false discovery rates with significance set to q < 0. 1. 30 With the significance set at q < 0. 1, the results should include at least 90% true positives.
The concentration of the hydrolytic product of the substrate, that is, p-nitrophenol, was determined by measurement of its absorbance at 405 nm after 3 minutes using a Sunrise microplate reader (Tecan, Gr€ odig, Austria). The absorbance readings were corrected for spontaneous hydrolysis by subtracting the absorbance of the negative control. The sampling time was found to be within the linear range of the reaction. All reactions were performed in triplicate. The half-maximal inhibitory concentration (IC 50 ) constants were determined using nonlinear regression in Prism, version 6. 07 (GraphPad Software, San Diego, CA). In silico analysis A docking study was carried out to decipher the preferred CES1 binding site of the seven bile acids for which we determined IC 50 values in the in vitro analysis, namely, glycocholate, taurochenodeoxycholate, taurocholate, deoxycholic acid, glycochenodeoxycholate, chenodeoxycholic, and cholic acid.
For this purpose, the coordinates of human CES1 in complex with taurocholate in the catalytic site and in the Z-site (Protein Data Bank code 2DR0) were obtained from the Research Collaborator for Structural Bioinformatics Protein Data Bank. The structure preparation and docking calculations were performed using CLC Drug Discovery Workbench (CLC Drug Discovery Workbench version 2. 0. CLC Bio-Qiagen, Aarhus, Denmark). Water molecules and cofactors were removed from the complex and the monomeric form of the protein was protonated at pH 7. 2. The binding sites were defined with a 15 A ˚radius around taurocholate, both in the catalytic site and the Z-site. To assess whether the docking program was able to reproduce crystallographic binding modes, taurocholate was docked back in the two binding sites.
Subsequently, the seven bile acids were docked into the defined binding sites assuming the protein to be completely rigid in all docking studies to reduce computational costs. A total of 100 conformations were computed for each compound and the first ranked conformation of each of the compounds, that is, the conformations with the lowest docking scores and the most stable interactions inside a cavity, were considered for further analysis. Analysis of binding was performed using the software Molecular Operating Environment, 2015. 10 (Chemical Computing Group, Montreal, QC, Canada).
RESULTS Lipid metabolites were measured either in positive or negative mode. In positive mode, 153 lipid metabolites were measured (Supplementary Material). They belonged to the following lipid classes: ceramide (Cer), cholesterol esters (CEs), diacylglycerol (DG), lysophosphatidylcholine (LPC), phosphatidylcholine (PC), phosphatidylethanolamine (PE), sphingomyelin (SM), and triglycerides (TG). In the negative mode, 61 lipids were measured belonging to: fatty acids (FAs), LPC, lysophosphatidylethanolamine (LPE), SN1, and SN2 classes. A measure of 13 individual bile acids (primary and secondary) and a panel of 32 amines were also measured. Predose metabolomics data were correlated with the PK profile of MPH and its metabolite RA (Table  1 ). Because the study subjects were of similar age (20-29 years) and body mass index (18-28 kg/m 2 ) evaluation of these covariates indicated that they did not possess enough variance to justify adding them to the model (data not shown).
However, there are at least two other benefits-for the local authority or academic institution, insofar as smart libraries will improve their global smart service quality in the field of information and knowledge; and, for the ICT industry, as the main provider of new technologies, compliant with the concept of smartness. What about the library staff? Where new technologies have already be implemented, the impact on library job skills and profiles is paradoxical and contradictory. Some profiles will need more skills (not only ICT but also management, communication, and social skills) while other profiles will disappear or be impoverished. Here is not the place to anticipate job developments or to make recommendations, but the experience with ICT implementation in the libraries during the last decades (library systems and databases, personal computers, internet, and the web, social media etc.
The quality control of mechanical parts is generally performed on a coordinate measuring machine (CMM). The choice of the number of points to be sampled, their distributions and their positions on the surface, as well as the association criterion remains unresolved. This paper studies the variation of the flatness defect with regard to the number of palpated points. The methodology begins by sampling a cloud of points on a CMM. Then, a modal analysis study is carried out in order to generate a modal surfaces (digitally deformed). Insertion of the coordinate cloud points of these modal surfaces in the GEOVERIF software will allow estimation of the flatness defect. The results of the measured flatness by the three fitting criteria (minimum zone, least squares and minimum volume) are compared.