dataesr/scientific-paragraphs-categorization huggingface dataset:
At the other end, best performances are obtained by pooling over time, especially when we consider the real datasets. This result is also interesting in the sense that best performances are achieved while no time information are kept in the features as they are totally translationinvariant. We make the hypothesis that this occurs because most audio scenes can be distinguished according to some global analysis (enhanced by pooling over time) of some recurrent patterns without the needs to look at some short-time single events, although these events may carry discriminative information. This rationale is also corroborated by the fact that MFCC-RQA features that are averaged over time performs reasonably well on the real datasets. While this approach works pretty well for the audio scene classes we have considered, we believe that features able to leverage on short-time events will be needed for fine-grained audio-scene classification.
D. More insights on the Rouen's dataset As one of our main contribution in this paper is to introduce a novel audio scene dataset, we discuss in the sequel our findings regarding this dataset. In Table  V , we have shown that mean average precision, obtained as an average over 20 trials, is 0. 9170. Table VI presents the normalized sum of all confusion matrices obtained from these 20 training/test splits. They have been obtained using the best performing HOG feature : namely the one with signed and unsigned orientations, without normalization factors of the histograms and fully pooled over time, the HOG being obtained with cell size of 8, 8 bins of the orientations and no average filtering. The average precision obtained from this matrix is 0. 915 and it is different to the mean average precision over the 20 runs as presented in Table  V.
Then, our experimental results on real datasets clearly show that our features achieve state-ofthe-art classification performances on several datasets. While our HOG-based feature is globally efficient, the overall pipeline for audio scene classification still lacks in discriminating some difficult classes. In order to further improve the scheme, some efforts are still needed. Our future researches focus on improving discriminative ability of HOG-based feature by working on the pooling strategy. The supervised learning paradigm may also be improved by taking into account an hierarchical taxonomy of the classes. We plan to investigate this taxonomy by learning it directly from the data.
20 different splits of the dataset into a training set and a test set. For all datasets except the toy ones, 80% of the examples have been used for training. For the toy problem, we considered only 40 training examples among the 200 available. Note that all features have been normalized so as to have zero mean and unit variance on the training set. The test set has also been normalized accordingly. All the parameters of the SVM are tuned according to a validation scheme. The C parameter is selected among 10 values logarithmically scaled between 0. 001 and 100 while the parameter σ of the Gaussian kernel e -x-x 2 2σ 2 is chosen among [1, 5, 10, 20, 50, 100]. Model selection is performed by resampling 5 times, the training set into a learning and validation set of equal size. The best hyperparameters are considered as those maximizing averaged performances on the validation set.
10 following locations : bus, busy street, office, open air market, park, quiet street, restaurant, supermarket, tube, tubestation. Recording has occurred at a rate of 44. 1 kHz and the number of examples available is 100 with 10 examples per class. Note that, the challenge's organisers have only made available the development dataset 2 C. East Anglia (EA) dataset This dataset 3 has been collected in the early 2000 by Ma et al. [26] at the East Anglia University. It provides environmental sounds coming from 10 different locations: bar, beach, bus, car, football match, laundrette, lecture, office, railstation and street. The length of each recording is 4 minutes and it has been recorded at a frequency of 22100 Hz. Similarly to the D-case dataset, we have split the recording in 30-second audio scene examples.
PERFORMANCES OF DIFFERENT FEATURES ON THE DIFFERENT DATASETS. BOLD RESULTS DEPICT BEST PERFORMANCES FOR EACH DATASET AS WELL AS RESULTS THAT ARE NOT STATISTICALLY SIGNIFICANTLY DIFFERENT ACCORDING TO A WILCOXON SIGNRANK TEST WITH A P-VALUE = 0. 005. TEXTURE DENOTES THE FEATURES OBTAINED FROM THE WORK OF YU ET AL. [13] WHILE MFCC-D-DD IS RELATED TO THE MFCC AND DERIVATIVES FEATURES. MFCC, MFCC-RQA, MFCC-900 AND MFCC-RQA-900 RESPECTIVELY DENOTE THE MFCC FEATURES, THE MFCC AND RQA FEATURES AT CUT-OFF FREQUENCY OF 10 KHZ, THE MFCC AND THE MFCC AND RQA FEATURES WITH UPPER FREQUENCY SET AT 900 HZ. THE HOG (FULL) AND (MARGINALIZED) ARE RELATED TO THE HOG FEATURES WHICH ARE RESPECTIVELY OBTAINED BY CONCATENATING THE HISTOGRAMS FROM ALL CELLS AND BY CONCATENATING THE TWO MARGINALIZED HOG FEATURES.
THE EFFECTS OF HOG FEATURE PARAMETERS. TWO DIFFERENT PARAMETERS HAVE BEEN EVALUATED: THE SIGN OF GRADIENT ORIENTATIONS IN THE HISTOGRAM COMPUTATIONS (SIGNED, UNSIGNED AND BOTH) AND THE INCLUSION (WITH OR WITHOUT) OF THE NORMALIZATION FACTORS. BOLD RESULTS DEPICT BEST PERFORMANCES FOR EACH DATASET AS WELL AS RESULTS THAT ARE NOTSTATISTICALLY SIGNIFICANTLY DIFFERENT ACCORDING TO A WILCOXON SIGNRANK TEST WITH A P-VALUE = 0. 005.
THE EFFECTS OF HOG FEATURE PARAMETERS AND THE IMAGE FILTERING PARAMETER. THREE DIFFERENT PARAMETERS HAVE BEEN EVALUATED: THE SIZE OF THE CELL FOR NORMALIZATION, THE NUMBER OF ORIENTATIONS IN THE HOG COMPUTATION AND THE AVERAGE IMAGE SIZE. BOLD RESULTS DEPICT BEST PERFORMANCES FOR EACH DATASET AS WELL AS RESULTS THAT ARE NOT STATISTICALLY SIGNIFICANTLY DIFFERENT ACCORDING TO A WILCOXON SIGNRANK TEST WITH A P-VALUE = 0. 005.
THE EFFECTS OF POOLING. THE NUMBER UNDER THE Freq AND Time LABELS DEPICTS THE NUMBER OF HISTOGRAMS ON THE FREQUENCY AND TIME AXES AFTER POOLING. FOR INSTANCE, THE FIRST ROW PRESENTS THE RESULT OF POOLING WHERE ALL HISTOGRAMS HAVE BEEN AVERAGED OVER THE FREQUENCY AXES. THIS POOLING CORRESPONDS TO THE RED POOLING IN FIGURE 3. BOLD RESULTS DEPICT BEST PERFORMANCES FOR EACH DATASET AS WELL AS RESULTS THAT ARE NOT STATISTICALLY SIGNIFICANTLY DIFFERENT ACCORDING TO A WILCOXON SIGNRANK TEST WITH A P-VALUE = 0. 005.
The 30-sec examples have been obtained by splitting a given signal into 30-second segments without overlapping. A summary of the dataset is given in TableIand Figure5presents some samples of CQT for 2 different audio scenes. The plots in this figure show typical characteristics of an audio scene of the class. For instance, in the bus's CQT, we can note the low-frequency line related to the bus's acceleration and deceleration. In the kid game hall scene, we see some high-frequency structures induced by kid screams.
Datasets features dim classifier Toy EA D-Case Rouen Texture 20 linear 1. 00 ± 0. 00 0. 57 ± 0. 13 0. 23 ± 0. 10 - Texture 20 gaussian 1. 00 ± 0. 01 0. 49 ± 0. 13 0. 19 ± 0. 09 - mfcc-d-dd 7800 linear 1. 00 ± 0. 00 0. 98 ± 0. 04 0. 51 ± 0. 13 0. 66 ± 0. 02 mfcc-d-dd 7800 gaussian 1. 00 ± 0. 01 0. 97 ± 0. 05 0. 49 ± 0. 15 0. 69 ± 0. 02 mfcc-d-dd 39 frame/majority 0. 47 ± 0. 04 0. 95 ± 0. 05 0. 32 ± 0. 12 0. 36 ± 0. 04 mfcc 3900 linear 0. 78 ± 0. 04 1. 00 ± 0. 01 0. 53 ± 0. 12 0. 67 ± 0. 01 mfcc 3900 gaussian 0. 77 ± 0. 03 0. 99 ± 0. 03 0. 52 ± 0. 10 0. 76 ± 0. 02 mfcc-900 3900 linear 0. 50 ± 0. 04 0. 91 ± 0. 07 0. 53 ± 0. 11 0. 60 ± 0. 02 mfcc-900 3900 gaussian 0. 50 ± 0. 04 0. 86 ± 0. 09 0. 56 ± 0. 12 0. 66 ± 0. 02 mfcc+RQA 37 linear 0. 51 ± 0. 02 0. 95 ± 0. 08 0. 54 ± 0. 09 0. 78 ± 0. 01 mfcc+RQA 37 gaussian 0.
Objectives The main objective of this study was to develop a representation of care trajectories over time for new cancer patients, using the data from the Poitou-Charentes Regional Cancer Registry information system, and to assess the reliability and validity of this representation by confronting trajectories derived from cancer registry data used by the algorithm with observed care trajectories documented from medical records. Since breast cancer is the most frequent cancer in women in France  [18]  and since it has been subject for many years to recommendations based on updated survey data  [19, 20] , we chose to make a first illustration of an application of the algorithm in the setting of non-metastatic breast cancer.
Information system The information system uses an Oracle 11 g database. The data management procedures were developed in the SAS environment (version 9. 3). The database of the cancer registry is a time-stamped relational database storing medical data with the date of occurrence of every event (such as date of admission, date when a biopsy was performed). Temporal intervals (such as the start, end and duration of a treatment) are not directly available but can be computed from the data. The cancer registration database results from the investigation of five types of electronic data sources in the Poitou-Charentes area and in neighboring départements (Data sources), where each data source comprises all facilities, centers or establishments involved in cancer care to ensure the completeness and the coverage of all areas of cancer care for patients residing in Poitou-Charentes area.
A single program manages data capture in the system, with distinction according to the formats of each source. The integration process of data from different sources for a given individual enables each source record to be linked to the individual to which it belongs, performed by an identity server (Computerised record linkage). A single dataset has been created for the study of temporal representation of care trajectories by extracting from two of the five data sources (anatomical pathology data and hospital discharge data), which concern 93 facilities or hospitals.
Data sources The Registry has established collaboration with more than 100 partners in the Region and in surrounding departments. Five types of data sources, using various terminologies to describe diagnosis, are routinely collected: Anatomical-pathology (AP) data (pathology data) which includes free-text reports related to one or several ADICAP diagnostic codes (Association pour le Développement de l'Informatique en Cytologie et en Anatomie Pathologiquethe French classification of lesions with topographical and histological axis  [21] ) (n = 28 facilities). Hospital discharge (HD) data recorded in the French medical information program (PMSI)  [22]  which includes ICD-10 diagnostic codes and CCAM medical procedure coded fields (Classification Commune des Actes Médicauxthe health insurance classification  [23] ) (n = 65 hospitals).
Computerised record linkage A primary function in the operation of a cancer registry is to bring together information describing the same individual from a variety of data sources. Because multiple notifications of the same tumour are expected if several sources of information are used, effective procedures for linking data on the same individual are very important, minimizing duplicate registrations of the same tumour and/or individual. So data extractions include patient identity (name, surname, birthdate …). When loading data in the registry information system, patient identities are integrated into an identity server which by way of a semi-automated process identifies data related to one and the same patient. The patient identification process, based on computerised record linkage  [16] , enables automated linkage (using deterministic rules), detection of ambiguities, duplicate searches and manual patient grouping or separation.
Cancer registration A notification algorithm  [24]  determines, for each patient, the tumours that should be notified to cancer registry staff according to registration rules for multiple primary cancers  [25]  and already validated tumours. Then each case is manually checked by registry staff by visual inspection of information sources, assessing the need to refer to a patient's medical record to register the tumour. Finally, all registered tumours contain relevant information, including date of diagnosis, ICD-O3 topography, ICD-O3 morphology and basis of diagnosis, and they are systematically related to the data sources. Following this step, the source data related to nonmetastatic breast cancers can be selected on the basis of this relationship to enable the representation of the care trajectories.
Representation of care trajectories The procedures for the temporal representation of care trajectories involve the following three stages: 1. Identification of tracer events in the care trajectory for each individual within the source datasets. 2. Chronological aggregation of events into states according to the level of granularity defined in step 1. 3. Representation of care sequences in a simple form (showing presence and order of states) and an extended form (including the duration of states) from which time lapses are calculated.
Identification of tracer events Events in the course of chemotherapy or radiotherapy are identified in HD data using ICD-10 diagnostic codes for chemotherapy (Z511) and radiotherapy (Z510) linked to the ICD-10 diagnostic code for breast cancer. For instance (Z511-C509) in source data refers to a "chemotherapy" event. Surgical eventstumour removal, tumour and lymph node removal, and lymph node removal alone are identified in HD data from the 3rd alphanumerical character in CCAM codes (defining the action performed) and linked to ICD-10 code for breast cancer. For instance (C502-QEFA001) in the source data refers to the event "tumour and lymph node removal". Biopsy and surgery events are identified in AP data from the first character in ADICAP codes (identifying the sampling mode) for breast cancer.
Evaluation of the representation of care trajectories The quality of the representations of care trajectories produced by the algorithm was assessed on a sample of patients for whom the care trajectories were constructed manually from data collected in the medical record (validation dataset). This dataset served as the reference standard for the evaluation of the representation of care trajectories. Each sequence was generated twice and independently from regional cancer registry data (automatically as "algorithm sequences") and from medical record (manually as "observed sequences"). The performance of the algorithm was assessed by confrontation of the sequences generated by the algorithm with the observed sequences for the patients (Figure  3 ).
Reconstruction of observed sequences Whereas the algorithm-based sequences were produced using solely the coded data from two electronic sources, the 159 observed sequences were manually reconstructed from the medical records, which collect all patient information produced and documented by staff involved in patient care. For this work the cancer registry staff systematically collected a copy of original pathology reports, surgery reports, hospitalisation reports or other relevant reports needed for the documentation of the main events in patient care trajectories. Each event occurring between diagnosis and the end of initial care provision for cancer was precisely documented (date and result of pathological investigation, date and nature of the surgical act, dates of start and end of chemotherapy, radiotherapy, HER2-targeted therapy, hormone therapy, number of sessions, alterations in treatment, occurrence of complications).
This documentation may be derived from several different institutions depending on the number of establishments frequented by the patient. The states making up the sequence were then manually captured in a single data table using the same rules implemented by the algorithm for the production of states. The observed trajectories were structured according to the simple form and the extended form, to enable confrontation with the trajectories produced by the algorithm. An endpoint for the end of treatment for each patient was determined manually from the last state in the initial care provision for the tumour. The endpoint of a trajectory can correspond to the death of the patient if it occurs before the end of the initial course of treatment of the cancer.
Similarity measures A similarity measure was performed on all pairs of trajectories using the Levenshtein Edit Distance (LED)  [32] , which enables comparison of chains of characters of different lengths using identical cost operations. The LED is equal to the minimum number of characters that need to be added, removed or replaced to switch from one chain of characters to the other. Each of these elementary operations is associated with a cost equal to 1. The LED function is available on SAS software using the function COMPLEV.
Discussion This study presents a method for the temporal representation of care trajectories for patients with nonmetastatic breast cancer using data from a regional multi-source information system. The method proposed enables identification of the main tracer events in a care trajectory, and also integrates the duration of each event, and the time-lapse between events making up the trajectory. The results of the experiment show that the algorithm is able to reconstruct the care trajectories automatically from the registry data without implementing a collection of relevant information in medical records, which would be very resource-consuming in this setting.
Performance of the algorithm in the ordering of states The ordering of treatment periods within the care sequences was correctly represented in 98% of cases. The three discrepancies observed were linked to absence of pathology evidence in the Registry data source, corresponding to non-coded sampling procedures in the pathology data (the practitioner did not code the tumour), or to coding errors or inconsistencies. Two of the dissimilar sequences nevertheless comprised all the treatment periods, because the absence of a pathology report on the surgical piece led to the creation of an intermediate surgery state in the sequence ("C" -surgery alone -rather than "D" -surgery and pathology evidence). An earlier study  [34]  implemented a text categorisation method using a machine-learning technique for the purpose of automatically categorising pathology reports solely on their content, which has demonstrated very good performances.
Limitations First, one limitation of this study is its restriction to only three of the 5 health "territories" in the Poitou-Charentes region, because of absence of data available relating to radiology in private practice. Indeed, private radiology units bill their activities on the basis of CCAM-coded acts performed, and they are not covered by PMSI. The integration of this activity into a database is essential because the share of radiotherapy sessions performed on a profit-making basis by private facilities was estimated to be 55% over the territory as a whole in 2002  [5]. There are no consequences on the performances of the algorithm because the evaluation was conducted on a sample of patients who received all care in three of the five health territories in the region covered by the public radiotherapy unit.
However, absence of radiotherapy data from private establishments in the cancer registry does not enable an evaluation of initial care trajectories extended to all breast cancer patients living in Poitou-Charentes area to be produced. We can however hope that this data will be integrated into the Registry database in the near future, since a reorganisation of the funding system for private-practice radiology is underway, and the units that at present code their activities according to CCAM will soon be required to produce standardised discharge reports as under PMSI  [35]. Second, representing care trajectories by way of a linear approach generates difficulties when therapies are concomitant and when they are administered over long periods. These problems can be managed by creating composite states, with the risk of increasing the number of different sequences.
To our knowledge this work is the first to assess the reliability and the validity of an algorithm for the representation care trajectories from cancer registry data. The method proposed in this work responds to needs expressed by institutions and health professionals of routine indicators aimed to improve the quality of patient care. The structured organisation in the form of a relational time-stamped database in the Poitou-Charentes cancer Registry was well-suited to the application of these techniques. The value of our study is that it models the care trajectory from on-going routine collection of data for exhaustive recording of incident cases of cancer. This approach is useful for routine manual registration of incident tumours, because it makes relevant information available to registry staff, information that is dated and presented in chronological order, facilitating visual inspection of the case and the identification of structures possessing complementary information, thus enabling cases to be recorded according to international standards.
These countries have undertaken to study waiting times and estimate reductions in time-lapses to treatment, which has become one of the aims in their cancer Plans. Certain specialised registries systematically record data concerning the initial treatment. This is the case, for instance, for breast cancer in the breast and gynaecological cancer Registry of the Côte d'Or area in France. This Registry used its database to supply information for a study conducted within the Francim network of cancer registries entitled "From diagnosis to first treatment: time-lapse to instatement of care for cancers recorded by the specialised registries in the Francim network 1999-2008", recently published by Inca  [44]. It showed that there were variations in time-lapse to treatment over this period. However, the majority of the general registries do not systematically collect information on care provision, and do not therefore have any scope for generalising these indicators unless a specific survey is conducted  [45].
Perspectives The routine production of these indicators will enable regular assessment of the match between care provided and official guidelines, the evaluation of time-lapse to treatment, and comparison of results with those reported in the international literature. This method can be applied by other cancer registries, conditionally on the availability of coded data sources using international classification of disease (ICD-O-3, ICD-10). The results presented in this study are in favour of a continuation of this work on other cancer localizations. Before implementing the algorithm on the populationbased cancer registry database for a new cancer localization, a further analysis of national guidelines and an evaluation on a new sample of patients are required. There are localisations where reducing time-lapse to treatment instatement is a major strategic orientation, and where providing care complying with guidelines requires the histological type to be identified, as in lung cancer.
Affinity determination of anti-hIgG1 activity by surface plasmon resonance Surface plasmon resonance (SPR) was measured using a BIAcore 3000 instrument at 25°C. hIgG1 (1500 RU) was immobilized on flow cells of a CM5-chip. The running buffer was HBSEP pH 7. 4 (20 mM Hepes, 150 mM NaCl, and 0. 005% P20). Regeneration was performed with 10 mM glycine, pH 3. 0. Measurements for affinity determinations of hIgG binders were performed with size-exclusion purified proteins Association and dissociation times were controlled at 3 and 10 min, respectively. Data were evaluated using Scrubber2 (Biologic software) and BIAeval software (BIAcore) using the steady state model and a global fitting procedure  (Karlsson and Falt, 1997).
ACKNOWLEDGEMENTS Circular dichroism data were acquired with a spectropolarimeter from the  IMPACT Core Facility Biogenouest, FRE-CNRS n°3478 Fonctionnalité et Ingénierie des Protéines (UFIP) -Faculté des Sciences et Techniques ou INSERM UMR892 -Centre de Recherche en Cancérologie (CRCNA) Institut de biologie (Nantes, France). This work was supported by the  Pasteur Institute (Paris, France) , with a postdoctoral 545.