dataesr/scientific-paragraphs-categorization huggingface dataset:
To check that this had no confounding effect on our study findings we compared weighted and unweighted ES of the measures. We weighted the study cohort to reflect the sex ratio from our local arthroplasty database (59. 8% female) and calculated the difference. This did not influence the results presented in the manuscript and we can therefore be confident in the analysis presented. The joint-specific scores (WOMAC score and the FJS-12) showed the highest responsiveness in terms of ES and SRMs compared to the KSS or the EQ-5D. The KSS and ROM measurement was able to detect change up to 1 year follow-up. However, the KSS barely improved (1. 4 points) between the 1 and 2-year follow-up and ROM remained constant at 120 (Table  II ). The two parts of the KSS (Knee and function score) also showed limited responsiveness in terms of ES and SRM between 1-and 2-year follow-up but performed substantially better during the first post-operative year.
In most of the outcome measures (WOMAC, ROM, KSS) SD decreased over time (halving between pre-op assessment and 2year follow-up). This is very important in the interpretation of ES, as the SD is the denominator. In the early recovery phase, floor/ ceiling effects are less pronounced because data are more normally distributed. This results in larger SDs (ES denominator). Thus, the same mean difference results in lower ES in the early recovery phase compared to the later phase. It is critical to consider this statistical artifact (which affects SRM in a similar manner) when interpreting the results in Table  IV. Beyond ES, RV allows for comparative analysis of individual scores. According to Fayers and Hays  32  RV gives the ratio of sample sizes "that would be required to detect the known group difference using one measure versus the other".
Therefore RV allows comparison of sample size needed for each instrument. Our data highlight that the EQ-5D requires 5 times as many patients as the WOMAC score to demonstrate baseline to 1-year change. It requires 10 times the number of patients compared to the WOMAC score to capture change between 1 and 2 years post-operatively. For longer term follow-up (2 years) the FJS-12 requires only one third of the number of patients compared to the WOMAC score. These are important considerations when powering outcome studies with PROMs. The good responsiveness to change of the FJS-12 is perhaps because this score is based on a more discerning construct. A 'forgotten joint' (i. e. , that the patient has no awareness of the affected joint during various activities of daily living) is very hard to accomplish.
Introduction and Motivations Model-Based Testing is one of the best ways to automate software testing. Even if the design of the model is a costly task, it is damped by the profits that can be made from it. Indeed, the model can be used to automatically compute the test cases, from simple input test data to complete sequences involving several steps. In addition, it is used to provide the oracle, namely the expected results of the test, thus deciding of the conformance between the system under test and its formal representation. Many model-based software testing tools exist, such as Smartesting's CertifyIt  [8] , Conformiq Designer  [17] , or STG  [9]. In practice, some of these tools work by automatically applying a structural test selection criterion on the model (such as the structural coverage of the OCL code  [23]  of the operations contained in an UML class diagram).
UML4ST -a subset of UML for Model-Based Testing The UML models we consider are those supported by the CertifyIt test generator, commercialized by the Smartesting company. This tool automatically produces model-based tests from a UML model  [4]  with OCL code describing the behaviors of the operations. CertifyIt does not consider the whole UML notation as input, it relies on a subset named UML4ST (UML for Smartesting) which considers class diagrams, to represent the data model, augmented with OCL constraints  [23] , to describe the dynamics of the system. It also requires the initial state of the system to be represented by an object diagram. Finally, a statechart diagram can be used to complete the description of the system dynamics. OCL provides the ability to navigate the model, select collections of objects and manipulate them with universal/existential quantifiers to build first-order logic expressions.
Running Example We illustrate the UML/OCL models that are considered using a simple model of a web application named eCinema. This application provides a means for registered users to book tickets for movies that are screened in a cinema. The UML class diagram, depicted in Fig. 2  contains the classes of the application: ECinema, Movie, Ticket and User. The ECinema class models the system under test (SUT) and contains the API operations offered by the application. This application proposes classical online booking features: a registered user may login to application, purchase tickets, view his basket, delete one or all tickets from his basket, and logout. Figure  3  shows the OCL code of the buyTicket operation. Upon invocation, the caller provides the title of the movie for which (s)he wants to buy a ticket.
In this context, the properties we designed aimed at expressing functional security properties on a smart card case study. It has also been exploited during the ANR OSeP project  3  , also in partnership with the Smartesting company, and funded by the Armaments Procurements Agency. This project focused on online and offline techniques for testing security (cryptographic) components. In both projects, we had the opportunity to address the following three questions: How easy it is to learn and use the test property language? (Q. 1) How does this approach compare to a functional approach such as Smartesting CertifyIt? (Q. 2) What is the interest of our industrial partners in our property-based testing approach? (Q. 3) For our experiments, we considered the case studies developed by our partners.
We started by designing test properties for the three considered case studies. The language turned out to be easy to learn, as the number of combinations is relatively small. With a little help from us, the validation engineers were able to express some security properties in natural language, rephrase them in order to fit the allowed combinations of scopes/patterns, and choose the appropriate entities to express these properties [Q. 1]. However, we noticed some pitfalls in its use. Indeed, the validation engineers tend to express test cases with the properties, e. g. by specifying sequences of events without taking care of the applicability of the sequence in general. For each case study, our industrial partners generated functional test cases using the Smartesting CertifyIt test generator.
Finally, we compared our test suite with the original test suite computed by CertifyIt. Figure  6  show the results we obtained. For each considered mutation operator we provide the overall number of mutants that were detected as: conform and not reaching an error state on the automaton (C-NE -it is either an equivalent mutant or applications are different, the AEG can be seen as a generalization of regular expressions. Our approach goes further as it uses a property description language that is close to a natural language.
In addition, throughout the management of the patient, it appears important to perform hemograms and to monitor evolution of leukemic cells every week during the induction phase and every month during the phase of maintenance. It now seems clear that vemurafenib is a treatment of choice for patients suffering HCL that are refractory to conventional chemotherapies including cladribine, pentostatin, rituximab or alpha interferon. Two independent clinical trials for large scale evaluation of the benefit of vemurafenib in refractory HCL patients are currently ongoing in the US (Clinical trial: NCT01711632) and Europe (eudract_number:2011-005487-13) that should extend and confirm the results obtained in isolated patients. Moreover, it would be also important to evaluate the impact of this treatment in first line after diagnosis of a Hairy Cell Leukemia.
Pyrosequencing Quantitative estimation of BRAF V600E mutants was performed with the therascreen® BRAF Pyro Kit (Qiagen). Briefly, PCR amplification of region flanking amino acid 600 of BRAF was performed on a PTC-200 thermal cycler (MJ Research,Waltham, MA). 1 μl of each isolated DNA was analyzed per run. Pyrosequencing was performed on the PyroMark Q24 platform (Qiagen) using the PyroMark Gold Q24 reagents. Pyrograms were generated with the PyroMark Q24 software (v. 2. 0. 6. ) and data were analyzed manually or with a plug-in tool provided by Qiagen. Sequences surrounding the site of interest served as normalization and reference peaks for quantification and quality control. Dispensation order was as follows: 5'-GCT ACT GTA GCT AGT ACG AAC TCA-3'. Two different "sequence to analyze" were used: 5'-YAY TGT AGC TAG ACS AAA AYC ACC -3' or 5'-CHC TGT AGC TAG ACS AAA ATY ACC -3' for manual analysis.
The main advantage of using ADC histograms would be its wide availability, simplicity and rapidity making it also suitable for routine clinical use. The aims of the present study were: 1) to evaluate whether very simple parameters derived from ADC histograms can be used similarly to DTI histogram parameters to assess the severity of cSVD, 2) to assess the magnitude of the effect of scanner upgrades or changes that necessarily occur in clinical studies over a large time scale-as compared to the effect of clinical scores, age and sex on ADC histogram parameters. For this purpose, clinical and MRI data from a large cohort of CADASIL patients evaluated in two different clinical centers and over a long time period were analyzed.
Subjects Data from 771 MRI scans performed in 348 CADASIL patients having a typical mutation of the Notch3 gene were used for analysis in this study. Patients were recruited from 2006 to 2012ina large cohort study performed in two referral centers (Lariboisiere Hospital in Paris and Institute for Stroke and Dementia Research in Munich). The detailed design of this study has been previously reported elsewhere. [29] The study was approved by an independentethics committee at both centres (Paris: Ethics Committee of Saint Louis Hospital, reference no. P020921-AOR02001; Munich: Ethikkommission der Med. Fakulta ¨t der LMU Mu ¨nchen), all patients gave a written informed consent to participate.
Clinical evaluation All subjects underwent a detailed neurological examination during the 2 hours before MRI, including the evaluation of the NIHSS, modified Rankin Scale, Barthel Index and Mattis Dementia Rating Scale. Patients had follow-up examinations with an interval of 18 months over a period of 3 years. directions). Eigen-vectors were obtained from all 23 directions for each voxel and eigen values were used for calculation of mean diffusivity (MD = Trace/23). Data obtained using other DTI sequences that were performed during the study were not included in this analysis.
Evaluation and impact of scanner upgrades A strong effect of scanner upgrade was detected on all diffusion parameters (measured both on DTI and DWI). This is illustrated on Figure  1  for the mean values of whole brain histograms after CSF removal. The latest scanner software upgrades Signa 11 new and Signa 12 were the only ones for which all parameters measured by DTI (MD) or DWI (ADC) histograms were found highly concordant, with intraclass correlation coefficients above 0. 9 (Table  2 ). On the contrary, the other scanner upgrades showed a smaller concordance, and the mean value and peak of the histogram were even very poorly correlated with Signa 09 version. Finally, Bland-Altman plots were also obtained for Signa 11 new and 12 versions of scanner since for the other versions, clear biases and deviations were expected.
Imaging was performed with a Bruker Multimode atomic force microscope controlled by a NanoScope IIIa controller. Samples were imaged in air, using tapping mode. The silicon cantilevers used had a drive frequency of ϳ300 kHz and a specified spring constant of 40 newtons/m (Olympus). The applied imaging force was kept as low as possible (A s /A 0 ϳ0. 85). For individual sigma-1 receptor particles, molecular volumes were determined using Scanning Probe Image Processor Version 5 (Image Metrology). It is well known that the geometry of the scanning AFM probe introduces a tendency to overestimate particle diameter. To minimize this probe convolution error, we used a particle threshold of 0. 1 nm to provide accurate measurements of diameter. For particles within complexes, particle heights and diameters were measured manually using the NanoScope software and used to calculate molecular volumes, according to the equation V m ϭ ͑h/6͒͑3r 2 ϩ h 2 ͒ (Eq.
This paper addresses the problem of audio scenes classification and contributes to the state of the art by proposing a novel feature. We build this feature by considering histogram of gradients (HOG) of an audio scene time-frequency representation. Contrarily to classical audio features like MFCC, we make the hypothesis that histograms of gradients are able to encode some relevant informations in a time-frequency representation: namely, the local direction of variation (in time and frequency) of the signal spectral power. In addition, in order to gain more invariance and robustness, histograms of gradients are locally pooled. We have evaluated the relevance of the novel feature by comparing its performances with stateof-the-art competitors, on several datasets, including a novel one that we provide, as part of our contribution.
Indeed, while MFCC can also be considered as features extracted from a timefrequency (TF) representation, they essentially capture non-linear information on the power spectrum of the signal. Instead, histogram of gradients of a TF representation provides information on the spectral power direction of variation. For instance, if an audio scene has been obtained in a bus that it is accelerating or decelerating, we expect the chirp effect present in the TFR to be captured and better discriminated by the histogram of gradients representation, than by MFCC. This property will be empirically illustrated in the sequel and it provides rationale that for audio scene recognition the novel feature we present is strongly relevant. Algorithms for audio scene recognition have to be validated and evaluated on some datasets.
In order to make comparisons of different designs of features including signal processing set-ups or different learning techniques possible, these datasets should be publicly available. The recent D-Case challenge  [10]  is an excellent initiative of this kind although its number of examples is limited (100 for the publicly available examples). Hence, another contribution we present is a new dataset for audio scene recognition. It is based on about 1500 minutes of recording on different locations (up to 19). This dataset is publicly available and we expect that it will become one of the standard benchmarks for audio scene recognition. The paper is organized as follows: we first describe the pipeline we propose for extracting our novel HOGbased feature. Then, as one of our contribution is also to introduce a novel benchmark dataset, we carefully detail all the datasets we considered for evaluating our feature and its competitors as well as the experimental protocol we employed for the comparisons.
A. Toy dataset For evaluating our features, we have created a toy problem which highlights the ability or the failure of  studied features (including HOG and competitors) in capturing power spectrum's direction of variation. As such, we have created a binary classification problem where signals from each class are composed of a localized linear chirp, respectively of increasing and decreasing frequency, defined as s(t) = Π [t1,t2] (t) cos 2π(at + b)t + n(t) with t ∈ [0, 1] and n is a centered Gaussian noise of standard deviation 0. 4, a = 1200, b = 0 for the class y = +1, a = -1200, b = 2400 for the class y = -1 and Π [t1,t2] (t) a function which value is 1 when t 1 ≤ t ≤ t 2 and 0 otherwise. We have set t 1 = 0. 4 and t 2 = 0. 6. Figure  4  depicts the CQ transform of representative samples of both classes.
D. Litis Rouen dataset This dataset we make publicly available 4  goes beyond the above ones in terms of volume and number of locations. Recordings have been performed using a Galaxy S3 smartphone equipped with Android by means of the Hi-Q MP3 recorder application. While such an equipment may be considered as poor, we believe that the resulting recordings would be similar to those obtained for real applications where cheap and ubiquitous microphones are more likely to be used. The sampling frequency we have used is 44100 Hz and the recording is saved as a MP3 file with a bitrate of 64 kbps. When transformed into raw audio signals, they have been downsampled to 22050 Hz. Overall, about 1500 minutes of audio scene have been recorded. They.
For the toy dataset, the upper frequency is set to the Nyquist frequency. For obtaining the final features, we average the obtained MFCC over batch of 40 windows with overlap of 20 windows and concatenated together all the MFCC averages and standard deviations. • Bag of MFCC-D-DD: these features are the same as above but in addition to the MFCC averages and standard deviations over a batch of 40 windows, we also concatenate to the feature vector, the average of first-order and second-order differences of the MFCC over these windows. These features should provide a better description of the dynamic behaviour of the signal. • Frame/majority-based MFCC-D-DD : For this ap-proach, we use the typical solution which consists in computing MFCCs and its first-order and secondorder MFCC derivatives for every 10ms-spaced frames of length 25ms.
Indeed, we believe that their choice was optimal for one dataset at the expense of genericity. For our HOG feature, we have set the following parameters. The CQT transform is computed, by means of the  [28] 's toolbox, on the same frequency range as the MFCC features and with 8 bins per octave. All other parameters have been kept as default as proposed in  [28]. The time-frequency representation is then transformed into a 512 × 512 image. The cells for the histogram computation are of size 8 × 8 and we have chosen 8 orientations. Note that it is also possible to consider the signed direction of gradient (leading thus to a histogram of size 16). As described above, histograms are normalized according to some norms, 4 normalization factors are computed by the vlfeat toolbox [29] and we have considered the possibility of using them as complementary features.
A Wilcoxon signed-rank test with a p-value of 0. 005 have been computed between the best competitor and the other approaches. For the toy dataset, the best performance is obtained by the Texture and MFCC-D-DD based features with a perfect classification. These features seem to be able to capture the discriminative part of the signal either by capturing the MFCC variations or by finding the good matching patch. We also explain this good performance by the fact that the two classes have high-energy welllocalized time-frequency structures. Except for these features, our HOG-based feature performs significantly better than its competitor RQA. MFCC is still able to discriminate the two chirps but less powerfully than the HOG feature, which natively captures the power spectrum variation. Interestingly, the best performing HOG feature is the one which considers all the histograms, despite the very-high dimensionality and the low number of training examples (40).
The East Anglia's dataset seems to be fairly easy and all features except the Texture-based and frame/majority approaches perform well with a slight advantage to MFCC. For the D-case challenge, the Texture, MFCC, MFCC-D-DD and MFCC-RQA features perform poorly, with performances around 50% and even around 20% for the Texture. However, with a more adapted upper-frequency of the spectral analysis, performances of the MFCC-RQA reach 68% of mean average precision. Marginalized HOG features perform significantly better than competitors with a gain in performance of about 19% when the range of frequency 1-10000 Hz is considered. This gain drops to 7% but is still consequent when the range of frequency 1-900 Hz is used for MFCC-RQA. However, we can note that using the full HOG representation induces a slight loss of performances and that it seems valuable to consider some HOG pooling.
We also want to highlight that the performance we report for MFCC-RQA-900 is slightly lower than those given in  [9]  and this is due to the fact our results are averages over 20 splits instead of a 5-fold cross-validation precision. For the Rouen's dataset we have introduced, the marginalized HOG features perform on par with MFCC-RQA and significantly better than all other competitors. Using a cut-off frequency of 900 Hz induces a larger loss of performance for these MFCC-RQA features. Also note that we cannot provide results using the Texture-based features as after one week, the feature computation was still running proving that approach is intractable even for medium-scale datasets. Again, MFCC-D-DD features with the frame/majority-based classifier fail to capture the discriminative information of the audio scene.
B. Analyzing HOG feature parameters In the first part of this experiment, we have investigated the influence of two parameters of the HOG features on the global classification performance. We have used the marginalized HOG features, as in the previous experiment, in conjunction with a linear kernel. The HOG features are composed of either signed, unsigned or both histograms eventually completed with the 4 normalization factors. Hence for each cell, the size of the feature ranges from 8 to 28 = 8 × 3 + 4. Since we have 64 rows and columns of 8 × 8 cells in the image, this results in feature vector of size ranging from 1024 to 3584. The results we obtain for the different datasets are presented in Table  III. We can note that the parameters we are evaluating clearly influence performances. Depending on the datasets, the variation of performances is in between 2% (for East Anglia) to 6% (for D-case and the toy dataset).
The most consistent feature seems to be the ones for which histograms are computed with both signed and unsigned gradient orientations and the normalization factors are not included. In the second part of the experiment, we have analyzed the effect of the average kernel size of the mean filtering, the number of orientations in the HOG as well as the size of the cell. The results are reported in Table  IV. We note that these parameters have different influences given the dataset. However, it seems that the choices of a cell size of 8 and 8 orientations for the HOG computation are a good default choice. The parameter with most influence seems to be the filtering kernel size. Depending on the datasets, the best kernel size varies from 1, which corresponds to no filtering to 15. According to this result, we thus suggest practictioner to tune this parameter value according to its own dataset.
Results for different sizes of pooling are presented in Table  V. Note that the first and last rows correspond respectively to the results obtained for the red and green pooling in Figure  3. Other rows are related to more general pooling form as in the blue pooling in Figure  3. A striking result can first be highlighted, regarding the importance of carefully selecting the pooling form : variation of performance between the worst and the best pooling form is at least 30% for all real datasets. Worst performance is achieved by pooling over frequency, which means that we average all the obtained histograms over the frequency, losing all informations about spectral contents. This finding is rather intuitive as we believe that the audio scenes can be mostly discriminated by their spectral contents and the local variations of their spectral contents.