dataesr/scientific-paragraphs-categorization huggingface dataset:
(2009a) ,  Mikesell et al. (2009b), and Nichols et al. (2010) , respectively. In the first example, we apply the SVD technique described above to a version of the acoustic synthetic dataset used by  Mikesell et al. (2009a)  contaminated with weakly-correlated Gaussian noise. We find that the virtual shot gather obtained through low-rank crosscorrelograms created by retaining only the largest singular value, as in a low pass filter, has a larger SNR, thus enhancing the reflected wave that is obscured by the noise in the standard virtual shot gather. In the second example, we apply SVD to the elastic synthetic dataset used by  Mikesell et al. (2009b). They show an improvement in the SNR in crosscorrelograms (and by consequence in the virtual shot gathers) in the presence of random noise, by stacking groups of crosscorrelograms under the assumption of lateral homogeneity.
DATA APPLICATIONS In this section we present three examples -acoustic synthetic, elastic synthetic, and field data. We first apply the SVD technique to a version of the acoustic synthetic dataset used by  Mikesell et al. (2009a)  contaminated with random noise. The virtual shot gather obtained through decomposing the crosscorrelograms using SVD and retaining only the singular value corresponding to the largest stacking coefficient in absolute value, as in a low pass filter, has a larger SNR thus enhancing the reflected wave that is obscured by the noise in the standard virtual shot gather. In the second example we use the elastic synthetic dataset used by  Mikesell et al. (2009b) , where we obtain improvements in the virtual shot gather's SNR by incorporating the SVD technique. Both synthetic wavefields were modeled using a spectral element method  (Komatitsch and Vilotte, 1998; Komatitsch and Tromp, 2002).
Acoustic synthetic example Here we apply the SVD technique discussed above to the same synthetic dataset used by  Mikesell et al. (2009a). Consider the 2-layer acoustic model shown in Figure  5. The top layer has velocity v 0 = 1250 m/s, the bottom layer has velocity v 1 = 1750 m/s, and density is constant throughout the model. A 2D array of 110 sources is placed to the left of the receiver line (Figure  5 ); the wavefield generated by each source is recorded at each receiver. The source is a 40 Hz Ricker wavelet. Now, we create a virtual shot gather as if there were a source at receiver r 1 using SI. The receiver gather for receiver r 1 is shown in Figure  6 (a). The GF between r 1 and each of the other receivers is obtained with SI. For example, consider the crosscorrelogram between receivers r 1 and r 18.
Each trace in the shot records (modeled and virtual) are normalized individually such that all direct arrivals have a peak amplitude of 1, and all gathers are displayed on the same gray-scale. We now add weakly-correlated Gaussian noise to the data, before crosscorrelation, to test the SVD method's robustness with respect to noise. The noise level, about one percent of the direct wave peak amplitude, was just enough so the refraction is lost and the reflection is very weak in the standard virtual shot gather. Similar to the clean dataset, the spectra in Figure  9 (a) and Figure  9(b)  show that there is one significant singular value and stack coefficient. Figures  9(c)-(d)  show again the standard and rank-1 crosscorrelograms for r 1 and r 18. Figures  9(e)-(f)  show the respective interferometric GFs.
Elastic synthetic example Next, we move to a more realistic, noisy, elastic synthetic data. Mikesell et al. (2009b)  used this dataset to show how to improve the SNR in crosscorrelograms and, consequently, in the virtual shot gathers, by stacking groups of crosscorrelograms under the assumption of lateral homogeneity. When lateral homogeneity does not hold, the same technique can be used if multiple-fold data is available. Here multiple-fold data means that for each source we produce multiple shot gathers that differ from each other only by the addition of a different realization of random noise. Assuming that multiple-fold data is available, we study four different ways to possibly improve the SNR in virtual shot gathers, including the SVD technique presented above, then apply and compare the four approaches to two different data folds.
Similar to the clean dataset in Figure  7 , the spectra in (a) and (b) show that there is one significant singular value and stack coefficient. The corresponding singular vector has the same waveform as G 1 , so we do not display it here. Again, note the enhanced reflection in (f) compared to (e), as in Figure  7. In (d), the crosscorrelations for sources 68-75 have their phase reversed. This is because there are three events merging in this zone in the original crosscorrelogram as seen in (c). Arrivals 1 and 3 dominate the crosscorrelations for sources 68-75 and their phase is approximately the opposite of the linear event 2 in the merging zone. As SVD preserves linearity, it simply reverses the phases for these sources in C 1. velocity layer underlain by a faster velocity layer as depicted in Figure  11.
The source and receiver arrays are 296 m and 152 m long, respectively, and the spacings are 4 m and 0. 25 m, respectively. The top layer is 20 m thick with velocities of v 0,P = 1000 m/s and v 0,S = 400 m/s. The lower half-space has velocities v 1,P = 1550 m/s and v 1,S = 600 m/s. In order to help attenuate freesurface multiples and better represent a near-surface of unconsolidated sediment, attenuation was included in the modeling. The Q values for the top layer are Q 1,P = 60 and Q 1,S = 10, and for the bottom layer are Q 2,P = 100 and Q 2,S = 30. The wavefield was modeled using an impulsive vertical impact source with a dominant frequency of 120 Hz. More details about this dataset can be found in  Mikesell et al. (2009b). We start by constructing virtual shot gathers from the noise-free data, and then we proceed to study a noisy version of this dataset.
This happens because the source wavelet is squared in the crosscorrelations  (Snieder, 2004; Wapenaar and Fokkema, 2006). } 20 m v v Q Q 0P 0S 0P 0S v v Q Q 1P 1S 1P 1S Figure  11 : Source-receiver geometry for group 1 of elastic synthetic example. Assume now we have a n-fold dataset. Each single-fold dataset consists of the clean data plus weakly-correlated Gaussian noise, with a noise level of about 40 percent of the direct wave peak amplitude, added to the modeled data before the crosscorrelations. Enough noise was added such that, for each single-fold data, the events lose coherency in the crosscorrelogram domain, and the arrivals are completely obscured by noise in the corresponding virtual shot gathers. To improve the SNR in virtual shot gathers, we study four different approaches. In approach one, we use a stacking procedure where virtual shot gathers are constructed from stacked crosscorrelograms, similar to what is done in  Mikesell et al.
(2009b). For each single-fold data we generate one crosscorrelogram. Crosscorrelograms are then stacked to form an n-fold crosscorrelogram, which are then used to construct a standard virtual shot gather. The stacking greatly enhances coherency in the crosscorrelogram space allowing the recovery of some of the events that were previously obscured by noise. Examples of such virtual shot gathers for two different folds (6 and 38) are shown in Figures  13(a ) and 14(a). Comparing these virtual shot gathers with the modeled shot gather for the noise-free data in Figure  12 (a), we see that some events are still missing. The second approach consists of reducing the noise by filtering the multiple-fold data for every source-receiver pair using SVD. Assume we have n-fold data. For every source-receiver pair, we collect the n corresponding recordings.
Data in these traces are the same other than the noise, thus forming a rank-1 dataset. Now we simply apply SVD to this set of similar traces to suppress the non-stationary energy, and use the first singular vector in the crosscorrelogram. Applying SVD in this manner is an alternative to the common offset stack (as in approach one) and has the same goal of increasing the SNR. The virtual shot gathers are then constructed as normal. Figures  13(b ) and 14(b) show the resultant virtual gathers for 6 and 38-fold data. These gathers have a better SNR than the respective gathers obtained through approach one, as seen in Figures  13(a ) and 14(a). The third approach is a combination of approach one and the SVD technique. First, the nfold crosscorrelograms are constructed through stacking, as in approach one.
The SNR is better than in the virtual shot gathers from approach one. In addition, it is also generally better than the gathers from the approach two. However, gathers from the third approach generally have a better SNR and fewer phase reversals/oscillations, particularly at larger offsets. We therefore conclude that the key place to apply SVD is to the crosscorrelograms and not to the common offset sections. In all the cases for this elastic dataset we used rank-1 approximations for all crosscorrelograms. We analyzed different ranks for the crosscorrelograms in two different ways. First, we looked at SVD-enhanced virtual shot gathers (approaches three and four) from crosscorrelograms of increasing ranks, but keeping the same rank number for all crosscorrelograms for a given virtual shot gather.
Second, we created virtual shot gathers from crosscorrelograms of different ranks: we defined thresholds and constructed each crosscorrelogram by keeping the stacking coefficients above a given threshold, i. e. , each trace in the virtual shot gather comes from a crosscorrelogram of different rank. We found that for this particular dataset there was visually no improvement over the rank-1 results. Therefore, we used the rank-1 crosscorrelograms in all cases. For other datasets, individual analysis of the stacking coefficient spectra may be advantageous. For this dataset, in general, additionally applying SVD to the crosscorrelograms leads to a better noise suppression than only stacking or SVD-filtering common offset data. Also, combining the SVD-filtered common offset data with SVD decomposed crosscorrelograms gives, in general, little or no improvements.
In this particular field dataset, the first singular value corresponds primarily to energy from the virtual refraction. Thus, contrary to the synthetic examples, the reflection phases were enhanced by ignoring the largest singular value. Even though the virtual refraction is not a physical arrival, it is an event with stationary energy in the crosscorrelogram, so it is not considered to be noise as far as the SI-SVD method is concerned. In addition, as mentioned above, the virtual refraction can be used to invert for physical parameters. As previously mentioned, a rank-1 matrix obtained through SVD will consist of one row that best represents the original matrix, thus capturing what is most in common among all rows. As explained in  Nichols et al. (2010) , the critical offset for this dataset is near the beginning of the source array, so most sources are at post-critical offsets.
e. approximately one every 30 m). These high density scans were co-registered using dedicated software, resulting in about 1. 5 billion points. The resulting accuracy (difference in position between traditional georeferencing and the 3D model) over 100 control points is less than 1. 7 cm. The reader is referred to  (Charbonnier et al. , 2013) , in the same archive, for further details. A rendering of the model using LEICA's TruView software is shown on fig. 2.
Trajectography During our experiments, we tested two ways of measuring the trajectory of the barge. The first one relies on external means. More specifically, tacheometers were placed at the ends of the tunnel for tracking the barge, which was equipped with targets. For the first campaign, 3 reflector prisms were used, in order to obtain both position and orientation information. For the second one, a single prism was considered. Synchronization with the other devices was made using a reference timer for the first experiment, and using the speaking clock, for the second one. We also tested an on-board solution: a commercial navigation system, which combines a differential GPS (Ashtech Proflex), an inertial navigation system (IXSEA Phins), and a Doppler log (RDI Explorer PA). GPS data is used to initialize a Kalman filter that integrates inertial and Doppler data.
Photogrammetric modeling The most classical method in structure-from-motion is bundle adjustment. To apply this technique, we consider the images of the right camera of the stereo rigs and we use a commercial software, namely PhotoModeler Scanner (PMS). The program first searches interest points in the images. Then it looks for correspondences between these points (homologous points). Finally, it estimates both the 3D structure of the scene, up to a scale factor, and the successive positions and orientations of the cameras. Geo-referenced points can be used to remove scale ambiguity and to fix the geometry of the model. The reader is referred to  (Charbonnier et al. , 2013)  in the same archive for more details about the construction of the 3D model.
The pairs of images taken by the panoramic rig are combined to create panoramas of the vaults, which is known as image stitching  (Szeliski, 2010). Both images are projected onto a cylinder, which seems well adapted to the context. Since images of the panoramic rig differ by a rotation, their cylindric projections are linked by a translation. This parameter is robustly estimated from corresponding points. The displaced images are then blended to form the panorama. In practice, we use the Hugin software to estimate the parameters of the transformation for a pair of representative images. Then, we apply the same transform to each pair of the sequence using the PanoTools library. This process is illustrated on fig. 8. As can be seen on the figure, the illumination of the vault is not as uniform as expected.
Introduction: Case-based reasoning (CBR) is an emerging decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting useful information from this database can help the CBR system providing more reliable results on the yet to be solved cases. Objective: We suggest a general framework where a CBR system, viz. K-Nearest Neighbour (K-NN) algorithm, is combined with various information obtained from a Logistic Regression (LR) model, in order to improve prediction of access to the transplant waiting list. Methods: LR is applied, on the case database, to assign weights to the attributes as well as the solved cases.
Introduction Case-based reasoning (CBR) is a problem-solving paradigm emerging in medical decision-making systems  [1]. Instead of relying solely on general knowledge of a problem domain, CBR utilizes the specific knowledge of previously experienced, concrete problem situations -also referred to as cases -to tackle new ones  [2]. More specifically, CBR methodology defines a general CBR cycle composed of four steps centered around a case database  [3]. First, the decision making process needs to identify, among the solved cases, those that seem to be the most similar to the considered unsolved case. Then, solve the new case relying on the knowledge extracted from the most similar solved cases. The third step consists in evaluating the suggested solution for the new case. Finally, if the solution is found satisfactory, the decision making process usually stores the part of the experiment likely to be useful for future problem solving.
In accordance with the traditional CBR view, the knowledge database contains cases, which consist in a problem-specific definition and construction. Thus, there are as many case bases as problems to be solved. Bergmann et al. overcome that problem by introducing the concept of utility  [6]. Similarity measures are not directly computed from the problem descriptions of new and previously experienced cases, they are computed with the description of their utility; utility description being specifically defined in accordance with the solution needed. Statistical analyses and regression modeling could be useful to introcuce utility description in CBR systems, by converting medical data sources -or data basesinto medical case bases. Regression models contain a part of knowledge which may be used to define utility description of cases and to perform problemspecific measures of similarity.
Study Population and Data Collection The study population consists of every incident ESRD patients in Brittany, limited to those who started an RRT (peritoneal dialysis or hemodialysis) between January the 1st, 2004 and December the 31th, 2008. Patients who received a preemptive transplant and patients who came back on the waiting list after a first transplant have been excluded. The dependent variable for the study was the patients registration on the renal transplant waiting list (e. g. registered on the waiting list: yes/no). The registration status was computed relying on the date of the first RTT as well as the date of registration on the waiting list. Only patients recorded on the waiting list within 12 months after inclusion on the REIN registry have been considered as registered patients.
A set of description factors have been defined according to data availability of the REIN database and the renal transplant scientific literature  [8, [11] [12] [13] [14]. All factors have been reduced to a binary value in order to simplify similarity calculation, in accordance with the traditional dichotomization procedure retrieved in the literature  [8]. Three categories of factors likely to be related to registration on the transplant waiting list have been studied: N Social and demographic factors: sex, age and current occupation at the first RRT. N Clinical and biological factors at the first RRT: existence of hypertension, diabetes, chronic respiratory failure, chronic heart failure, ischemic heart disease, heart conduction disorder or arrhythmia, positive serology (HCV, HBV, HIV), liver cirrhosis, disability, past history of malignancy and hemoglobin as ,11 g/dl and $11 g/dl.
Data description: Training, Setting and Evaluating sets The initial population included 1647 patients who began an ESRD treated by dialysis (652 (41%) women and 995 (60%) men). Among them, 350 i. e. , 21%, have been registered on the waiting list of renal transplantation in the first year following the start of RRT. Unfortunately, patients' data with respect to the selected explicative variables (Cf section 0. 2 for further details), were not always complete or fully available. Since, logistic models cannot deal with missing data, we decided to restrict this analysis to a subset of patients with no missing data. Thus, the study population was reduced to 1137 patients with complete data, which only represent 70% of the initial population. It is worth mentioning that the general caracteristics of this population remain similar to the original population.
The training set P T is dedicated to the LR, while the setting set P S is used to estimate an appropriate K-value of the K-NN algorithm. Finally, the training database, the setting database and the evaluation database are built relying on a random sampling for the set population set, such that: DP T D~DP S D~DP U D~379 It is worth mentioning that no specific filtering was used to obtain the same number of patients in all three databases. It is a simple coincidence that occurred after discarding patients with incomplete data. A Pearsons chi-squared test was performed to verify that all three sets share common characteristics. The chisquared test showed no significant difference between the three databases (data not shown).
Namely, the objective is to study the behavior of the prediction tools when the knowledge database contains factors not related to the prediction object. Moreover, in every scenario we evaluate the benefit of automated variable selection for LR before simulating the algorithms. Thus for every scenarios, we describe two subscenarios. We refer to them in simulations as the sub-scenarios Prediction using all attributes and Prediction using selected attributes. All scenarios and algorithms are summarized in Figure  1. All performance results are presented in terms of the receiver operating characteristic curve (AUC). In order to compute confidence intervals of AUC results, a bootstrap resampling procedure is performed  [17]. Thus, the probability distribution of AUC statistic is simulated by 500 random samples from the original evaluation database.
Results Table  1  shows the weights of attributes calculated from the Wald statistics using the regression coefficient estimates of the LR, as defined in section 0. 4. 2, and their respective standard deviations. Both sub-scenarios, summarized in Figure  1 , are considered where estimates are conducted after (or without) a stepwise selection procedure on the set of explicative variables (viz, attributes). The results of Table  1  consider first the case database with only 19 attributes relevant to our problem (referred to as before adding of 50 random factors). Then, 50 random attributes are added and the computations of both sub-scenarios are once again repeated. As expected, the attributes have a different impact on the registration. Their respective impact reflects on the performance of the K-NN algorithm through the values of the weights of attributes.
For the author, results related to CBR methods testify to the solid diagnosis capacity of CBR in examining healthy data. Our results support this conclusion since we have shown that CBR method present predictive performances equivalent to those obtained by LR. This paper shows however that it is true only if the considered attributes are well chosen and reliable regarding the problem to solve. Nugent et al presented the first association between CBR and LR in 2009 with a methodology called KLEF for Knowledge -Light Explanation Framework  [25]. The method describes how gaining high-level knowledge by a top-down mechanism using logistic regression. LR is used a posteriori to define one nearest neighbour from cases retrieved by a K-NN algorithm. LR in the present study was used differently. As a matter of fact, the logistic model was directly fitted from the overall knowledge database.
In the present study, LR is also used to perform attribute selection and attribute weighting. However, we proposed in addition to introduce Pearson residuals to weight the cases in the design of our K-NN algorithm. In our opinion, Pearson residuals based case weighting help, with attribute weighting, to the cases' description and specification when defining problem-specific knowledge  [6]. Thus, the model built by LR defines an archetype of registered and not registered patients in the knowledge database, and LR residuals reflect the adequacy of each patients with regard to the archetype. Relying only on regression coefficients or stepwise selection to define the cases as well as the problem utility would consider that all patients match perfectly the LR archetype. We know for a fact that it is not true.
However, the hybrid CBR system combing both attribute weighting and case weighting seems to be very robust to artifacts in the database that might occur in all realistic scenarios. From our point of view, this interesting observation provides new perspectives for future CBR system, particularly for integrating CBR systems into large and unspecific knowledge database such as data from electronic health records  [4, 28]. Finally, we join Huang et al's opinion as we believe that CBR is an explicit problem solving methodology. We believe that an association between LR and CBR systems improves comprehensiveness of problem-solving processing. This latter provides the users with more reliable information about relevant decision factors and case utility. Thus, the integration of bio-satistical analyses, widely used in the medical research, may also help in the dissemination and development of CBR decision support for medical practice.