dataesr/scientific-paragraphs-categorization huggingface dataset:
The third phonemizer backend is Segments  (Forkel et al. , 2019) , a Python package providing Unicode Standard tokenization routines and orthography segmentation. It relies on a grapheme to phoneme mapping to generate the transcription. This backend is mostly useful for lowresource languages, for which users with linguistic expertise can write their own mappings. Six languages are provided as examples with phonemizer: Chintang, Cree, Inuktitut, Japanese, Sesotho, and Yucatec. Text to phones transcription is a critical need in different applications related to natural language and speech processing. So far, the phonemizer package has been used in the preprocessing pipeline of various deep learning text to speech systems  (Ideas Engineering, 2021; Mozilla, 2021; Watanabe et al. , 2018). It has also been used as a preprocessing step in word segmentation studies regarding the role of speech prosody in segmentability  (Ludusan et al.
, 2017 ) and the psychology of child development  (Bernard et al. , 2020; Cristia et al. , 2019). A phonetic transcription generated by the package was used to evaluate a phone discrimination task for the Zero Speech Challenge 2017  (Dunbar et al. , 2017). The phonemizer is also very suitable to prepare datasets for their use with the Kaldi speech recognition toolkit  (Povey et al. , 2011) , where a phonetic transcription of text is a requirement for various algorithms. The package can also be used to generate forced alignments of speech corpora, an important part of the speech-related research pipeline whereby an acoustic signal is segmented and aligned with a text transcript. The most impactful software in this field  (McAuliffe et al. , 2017; Rosenfelder et al. , 2014)  requires a pronunciation dictionary to transcribe words into phonemes.
Such dictionaries, when available, can be non-exhaustive, thus requiring experimenter transcription, model training, and/or data exclusion. Replacing the dictionary by the use of the phonemizer can therefore improve the overall pipeline and alignment quality for supported languages. Finally, the phonemizer shows promises for the linguistic analysis of low-resource languages, where a major problem is the lack of grapheme to phoneme mapping  (Barth et al. , 2020)  or comprehensive pronunciation dictionaries  (Cristia et al. , 2020; Johnson et al. , 2018). In such cases, compiling a grapheme to phoneme map to be used by the Segments backend would be easier and more efficient than compiling an exhaustive pronunciation dictionary.
Acknowledgements We are thankful to  Alex Cristia , who initiated this project, and to  Emmanuel Dupoux  for his support and advice. We also thank the phonemizer users for their bug reports and features requests. This work is funded by the  European Research Council  ( ERC-2011-AdG-295810 BOOTPHON ), the  Agence Nationale pour la Recherche  ( ANR-17-EURE-0017 Frontcog ,  ANR-10-IDEX-0001-02 PSL ,  ANR-19-P3IA-0001 PRAIRIE 3IA  Institute), and grants from  CIFAR (Learning in Machines and Brains) ,  Facebook AI Research  ( Research Grant ),  Google  ( Faculty Research Award ),  Microsoft Research  ( Azure Credits  and Grant), and  Amazon Web Service (AWS Research Credits).
Stationary Solutions The stationary solutions {V (e) , V (i) , R (e) , R (i) } of (6) can be explicitly obtained for the mean membrane potentials as V (e) = - g (ee) 0 ∆ (ee) 0 2π , V (i) = - g (ii) 0 ∆ (ii) 0 2π ; (7) while the instantaneous population rates are the solutions of the following quadratic system g (ee) 0 R (e) τ m -g (ei) 0 R (i) τ m = -I (e) 0 + ε πR (e) τ m 2 -V (e) 2 (8a) g (ie) 0 R (e) τ m -g (ii) 0 R (i) τ m = -I (i) 0 + ε πR (i) τ m 2 -V (i) 2 (8b) where ε = 1/ √ K is a smallness parameter taking in account finite in-degree corrections. It is interesting to notice that the parameters controlling the structural heterogeneity ∆ of the mean membrane potentials reported in (7). The solutions of (8) can be exactly obtained and the associated bifurcations analysed by employing the software XPP AUTO developed for orbit continuation  (Ermentrout, 2007).
FUNDING AT received financial support by the  Excellence Initiative I-Site Paris Seine  (Grant No  ANR-16-IDEX-008 ) (together with HB), by the  Labex MME-DII  (Grant No  ANR-11-LBX-0023-01 ) and by the  ANR  Project  ERMUNDY  (Grant No  ANR-18-CE37-0014 ) (together with MdV), all part of the  French programme "Investissements d'Avenir".
In this paper we introduce a dataset of environmental information obtained via indoor and outdoor sensors deployed in the SMART Infrastructure Facility of the University of Wollongong (Australia). The acquired dataset is also made open-sourced along with this paper. We also propose a novel approach based on an evolutionary algorithm to determine pairs of correlated sensors. We compare our approach with three other standard techniques on the same dataset: on average, the accuracy of the evolutionary method is about 62,92%. We also evaluate the computational time, assessing the suitability of the proposed pipeline for real-time applications.
systems difficult to design through traditional methods, as not all states of the system are known a priori. In this paper, we propose and evaluate a novel methodology to dynamically exploit the correlations between sensors to provide experts with automatic information about the building state. Studying how sensors are correlated or uncorrelated allows detecting variations of behavior in the building that might be indicative of an anomaly such as a malfunctioning air conditioning system, or a window that stays open during the night  (Houssin et al. , 2020). Because those anomalies can occur unexpectedly, the correlations must be processed in real-time (i. e. within fixed time constraints). The contribution of this article is twofold: • we introduce an open dataset of one year of heterogeneous environmental information acquired through internal and external sensors deployed in the SMART Infrastructure Facility at the University of Wollongong (Australia).
Making this dataset open sourced will allow the scientific and industrial communities to have a consistent set of environmental information to be used for simulations, testing applications and comparing methodologies in a smart building context; • we propose a novel solution based on an evolutionary algorithm to detect in real-time highly correlated pairs of heterogeneous sensors. Our proposal addresses the properties of openness, heterogeneity and unpredictability  (Guastella and Valenti, 2018). The novelty of our contribution is: • proposed an approach to determine pairs of highly correlated sensors whose information dynamics are similar in time; • it does not make any assumption on the topography of the environment where the sensors are deployed. This property makes the solution generic so that it can be applied regardless of the building configuration.
• proposed a methodology that uses heterogeneous information to measure the correlation between pairs of sensors. Moreover, our approach exploits sensors whose type of perceived information is not known a priori. The rest of this paper is organized as follows: Section 2 presents the proposed approach, describing how it is capable of determining pairs of correlated sensors based on their perceptions. Section 3 presents the results obtained from our method using the data acquired from the SMART Facility. In Section 4, we conclude and point out some future perspectives.
METHODOLOGY In this section, we present a novel approach that reminds an evolutionary algorithm for determining correlations among sensors in smart buildings. Along with this method, we present three other original pipelines to compare the results obtained from the evolutionary approach and also to illustrate techniques that can be used to exploit the introduced dataset. Fig. 1  shows the main steps of our methodology and the three other pipelines. The described techniques do not require any a priori information on the topology of the environment. All the input parameters we used have been obtained on an experimental basis; our results refer to their best configuration. The following section describes the proposed pipeline. Sections 2. 2, 2. 3 and 2. 4 present the techniques used to compare the results obtained through the evolutionary method.
Pearson based Technique The third technique (diagram in Fig. 1 ) uses the Pearson correlation coefficient  (Zhou et al. , 2016)  and wavelet decomposition  (Sciortino et al. , 2017). This pipeline iterates over windows of 60 samples, consecutive in time, for all the pairs of sensors in the dataset. The first step of the pipeline consists of calculating the wavelet decomposition of both the time series of the current sensors pair. The wavelet decompositions are calculated for the entire time series using the Daubechies wavelet of order 4  (Vonesch et al. , 2007). Then, the technique iterates all the information in blocks of 60 samples for all pairs of sensors. At each iteration, the Pearson coefficient is calculated from the wavelet coefficient of both current sensors, for the current time instant.
This process results in a set P = {ρ 1 , ρ 2 ,. , ρ n } of Pearson coefficients. The correlation between the two sensors considered in the current iteration is calculated as the average of the Pearson coefficients in P. The last step consists of determining which pairs of sensors have been correctly matched. For this purpose, we applied Otsu's thresholding technique  (Otsu, 1979)  to calculate a threshold value used to separate the set of correlation coefficients into two separate and disjointed classes. Once the two classes have been obtained, we identified the correctly matched pairs of sensors. The computation time of this technique is lower as compared to the previous ones (it takes just about 1 second to process the entire dataset), however, the accuracy is considerably lower, returning only 22% of correctly matched pairs.
Experimental Context The SMART Infrastructure Facility at the University of Wollongong (NSW, Australia) was created in 2011 and provides specialist laboratories for 150 staff and 200 postgraduate research students. In 2018, the  In addition to the existing Droplet sensors, mobile sensors based on a Pycom platform have been developed to capture the temperature and humidity at a different location inside and outside the building, every 2 minutes, thus enabling the rapid deployment and testing of different scenarios. Fig. 3  show the two types of sensors used for the experimentation. The comparison between the Droplet and Pycom modules for both temperature and humidity is shown in Table  1 , as these the two data parameters used to perform correlation in this study. The scenario investigated in this work is illustrated in Fig.
Evaluation Method The techniques proposed in the previous section have been validated in four versions of the presented dataset, containing different sensors deployed in the SMART Facility building: • Test #1: all sensors, inside and outside the build-  ing; • Test #2: only the sensors inside the building; • Test #3: external sensors and internal sensors on the 2nd floor; • Test #4: external sensors and internal sensors on 1st floor. The techniques have been evaluated on the same set of sensors but using different types of information: (i) temperature, (ii) humidity and (iii) temperature and humidity combined. This last combination was tested only on the evolutionary technique because it is the only one capable of integrating heterogeneous information. We used two criteria to assess the quality of the solution obtained by the proposed techniques: 1.
Discussion The techniques described show promising results on the data acquired from the SMART Facility building. Among these, the evolutionary one allows addressing the need for on-line computation. Moreover, this evolutionary approach allows integrating heterogeneous, which is not possible by the other presented techniques. The criteria to determine the correlation between sensors allow the domain expert to extract useful knowledge from the perceived information: by using the criterion based on the correlation between internal or external sensors it is possible to determine the topography of the environment, discriminating between internal and external sensors. The criterion based on Voronoi tessellation allows determining pairs of sensors that have a high informative correlation and are also in the immediate vicinity.
CONCLUSIONS The contribution of this article is twofold: firstly, we present a novel and open dataset of environmental information acquired from indoor and outdoor sensors deployed at SMART Infrastructure Facility at the University of Wollongong, Australia; secondly, we present a novel evolutionary approach to determine in real-time the correlation between pairs of different sensors. This correlation is computed by using the information coming from the sensors and two criteria: the former based on the presence of both sensors inside or outside a building, the latter on the spatial distance among the sensors themselves. We experimentally verify that this allows determining accurately correlated pairs of sensors. These techniques can be applied for different smart building applications such as environmental monitoring to optimize energy consumption or anomalies detection.
, ±0. 4% RH 0 to 100%, ±0. 008% RH SMART building was equipped with a Droplet environmental sensors from Nube iO. A fleet of 140 sensors monitor the temperature, humidity, atmospheric pressure and movements inside every room inside the Facility. These sensors are battery-powered and rely on a long-range, low-power LoRaWAN network for transmitting data every 30 second. The LoRaWAN gateway then transmits the incoming data to a local database. The physical location of the sensors and the open database containing all the records and time series data for 2019 are publicly available and detailed in (Barthélemy et al. , 2020b).
Numerical analysis. The numerical analysis of the present study was conducted on a personal computer (MacBook Pro, Intel Core i5, 1867 MHz, 8 GB RAM, macOS Catalina, MATLAB R2019a). Here, we describe the details of the statistical evaluation of the entangled-and correlated-photon mixed strategy for CMAB problems. For emulating the entangled photons and the two slot machines, we utilized uniformly distributed pseudorandom numbers generated by Mersenne twister. When the interval of happy and non-happy hours is specified by the interval T, the initial starting time of the happy hour was randomly determined by a uniformly distributed random natural number between 1 and 2 × T, for each repetition. The results shown in Figs. 3  and 4  were obtained by averaging over 1,000 such randomly arranged repetitions.
ACKNOWLEDGMENTS Gilles Cibiel ,  Serge Galliou  and  Enrico Rubiola  are acknowledged for fruitful discussions. S. W. acknowledges funding from  Labex FIRST-TF  under contract number  ANR-10-LABX-48-01. The work is supported by the  French ANR  project  " SPINNET "  ANR-18-CE24-0012. P. T. acknowledges support under the  Cooperative Research Agreement Award  No. 70NANB14H209 , through the  University of Maryland. M. R. acknowledges support from  Spanish MICINN  ( PGC2018-099422-A-I00 ) and  Comunidad de Madrid (Atracción de Talento  Grant No. 2018-T1/IND-11935 ).
Introduction Designing systems for automatic surveillance and traceability of medical devices (MD) using Electronic Health Records data is a great challenge facing manufacturers, regulatory agencies and healthcare providers. To develop this kind of systems, thesauri and knowledge databases describing MD (MD-KDB) and their features allow to address interoperability issues, to perform indexing and information retrieval tasks as well as automatic reasoning. However, we are still far from having a standard, unified and worldwide MD-KDB since this domain is quite complex, vast, subject to industry competition and under different regulations between countries. In the perspective of reusing real world clinical data to automate the monitoring of orthopedic MD, we carried out a comparison of the main MD-KDB currently available in France.
Material and method We compared four MD-KDB : ANSM  [1] , Gudid  [2] , Exhausmed  [3]  and CIOdm  [4]. For that, we performed a quantitative comparison of the number of devices available and the number of features available in each of these databases and we compared MD-KDB features on availability, consistency and the thesaurus used (Cladimed  [5] , LPP  [6] ,  GMDN [7] , CND  [8] ) through an example.
size amongst the different machine-defined clusters. When we merge these multiple clusters into two large preliminary clusters to provide a binary classification, an accuracy of ∼ 87% is reached using an imbalanced dataset, matching real galaxy distributions, which includes 22. 7% early-type galaxies and 77. 3% late-type galaxies. Comparing the given clusters with classic Hubble types (ellipticals, lenticulars, early spirals, late spirals, and irregulars), we show that there is an intrinsic vagueness in visual classification systems, in particular galaxies with transitional features such as lenticulars and early spirals. Based on this, the main result in this work is not how well our unsupervised method matches visual classifications and physical properties, but that the method provides an independent classification that may be more physically meaningful than any visually based ones.
It is doubtful that the current data and human labelling abilities and speed could facilitate an unbiased and extensive labelled dataset for a supervised machine to 'correctly' classify the potentially unknown patterns within the data of future surveys. Furthermore, this supervised learning will always miss unusual systems. For the reason discussed above, we use unsupervised machine learning which is trained without any prior knowl-edge (e. g. , galaxy labels, such as Hubble types). This approach is able to give us suggestive classifications from the machine's perspective based upon input features. However, with an unsupervised machine learning technique it becomes more challenging to have a 'sensible' classification, that is one with more consistency with human opinion, when the dimensionality of a feature space becomes high (curse of dimensionality,  Bellman 1954; Keogh & Mueen 2017).
1). The value of λ is equal to 0. 1 in our case. As shown in Fig. 1 , during the training of the VQ-VAE, we interpolate an unsupervised instance-based clustering algorithm called 'k-medoid clustering'  (Maranzana 1963; Park & Jun 2009)  to obtain two preliminary clusters using a flattened index map. The two clusters are then used for measuring a silhouette score to evaluate the performance of the initial clustering. This step is simply to make VQ-VAE intentionally extract features that can not only be used to reconstruct the input images but also be well separated into at least two distinctive groups in feature space. The Hamming distance  (Hamming 1950 ) is used as the distance metric as our data is represented by the indices of the vectors in the codebook whereby the number itself only represents a category rather than a real value of the vector (more description in Section 2.
Data Sets The imaging data used throughout this work is from the Sloan Digital Sky Survey (SDSS) Data Release 7  (York et al. 2000; Abazajian et al. 2009 ) with a redshift cut of z < 0. 2. In order to focus on the impact of galaxy shape and structure to morphological classifications, we utilise monochromatic r-band images. An extension including colour and other factors is some to consider for the future. Here we are focused on single-band morphological classification on features seen and not in general a physical classification that might result from considering galaxy colours and colour distributions. To examine what types of systems our classification clusters contain, as well as to have the flexibility within the data distribution in our datasets, we use morphology labels defined by T-Type (de Vaucouleurs 1964) and the probability of being a barred galaxy (P bar ).
Both quantities are obtained using deep learning techniques from Domínguez  Sánchez et al. (2018, hereafter, DS18). We define eight labels including barred galaxies that contain significant features shown in the Hubble morphological system: ellipticals (E), lenticulars (S0), early spirals (eSp), late spirals (lSp), irregulars (Irr), barred lenticulars (SB0), early barred spirals (bar eSp), and late barred spirals (bar lSp). The comparison of the classification scheme is shown in Table  2 ; in which, S0, eSp, and lSp are separated into barred and non-barred galaxies based on the value of P bar. We additionally include labels of irregular galaxies from three other works:  Fukugita et al. (2007) ,  Nair & Abraham (2010) , and  Oh et al. (2013)  to provide more irregular galaxies in our database.
The morphological labels in our datasets are not This work E S0 eSp lSp Irr E S0 -, S0 S0/a -Sab Sb -Sdm Irr DS18 -3 -2, -1 0 -2 3 -8 10 Table  2. The classification scheme used in this work and in Domínguez  Sánchez et al. (2018, DS18; presented in T-Type). In DS18, they define the T-Type of -3 for ellipticals (E), -2 for lenticulars at the early stage (S0 -), -1 for lenticulars at the intermediate to late stages (S0), 0 for S0/a, and the positive values of T-Type are for different stages of spirals. Finally the T-Type of 10 represents irregular galaxies (Irr). used for training our machine, but to prepare an appropriate dataset with a specific data distribution, and as a way to examine the obtained clusters in terms of these types. To investigate the differences in the classification systems defined by humans and those from a machine, as well as potential application within our unsupervised machine learning technique in future surveys, we prepare two different datasets: which are 'balanced' and 'imbalanced'.
In the balanced dataset, we artificially allocate the same number of galaxy images to each morphological type. The eight human defined morphological types have visually distinctive differences from each other; therefore, the purpose of this arrangement is to allow our VQ-VAE consider fairly the characteristics of each morphology type when extracting the representative features from input images. Otherwise it is possible that some type of bias would result if the distribution of the types we select are input into our VQ-VAE in the same fraction as they are found in the nearby universe. In this case we would find that the late-type disks would dominate over early disks and ellipticals (e. g. ,  Conselice 2006). On the other hand, it is of great importance to know how an unsupervised machine learning technique can be applied in future surveys to explore a large scale of unknown galaxies' morphology in an 'as is' situation.
That is, we need to know how our VQ-VAE performs when galaxies are inputted from imaging observations of the real universe with no balancing. For this goal, we set up the 'imbalanced dataset' with a realistic distribution in terms of galaxy morphological types which follows the distribution of nearby galaxies at z=0. 033-0. 044 as presented in  Oh et al. (2013). The type distributions of the balanced and imbalanced dataset are shown in Fig. 3.
The type distributions of the balanced (left) and imbalanced (right) datasets. The latter follows the distribution of nearby galaxies  (Oh et al. 2013 ). The number shown above the coloured bar represents the fraction of the type in all data. The fraction of barred galaxies are highlighted with hashed lines. The orange and light blue colouring represent early-type galaxies and late-type galaxies, respectively. noises as our selection threshold. Any pixel value is below this criterion the pixel value is set as 0  (Martin et al. 2020). Whilst this will remove noise, it will also potentially remove outer fainter portions of the galaxies themselves. However, this will retain the brighter portions of the inner parts of galaxies where classification is done in any case. Removing this fainter light does not have an effect on our measurements as it would if we were measuring for example surface brightness profiles.