dataesr/scientific-paragraphs-categorization huggingface dataset:
The question of whether grammatical relations are coded for nouns, through constructions, was left open for investigations, which have been conducted using a query language based on regular expressions. 3 n intonation unit is a segment of speech that has a coherent intonation contour  (CHAFE, 1994) , and is delimited by its boundaries  (CRUTTENDEN, 1997) , which bear a 'boundary tone' (PIERREHUMBERT;  HIRSCHBERG, 1990). In Kabyle, Intonation Units are marked by one or more of the following cues: 4.
Grammatical relations My approach does not consider as a given the fact that in Kabyle, grammatical relations are encoded on all nominal/pronominals. Indeed, as shown in  Mettouchi (2013 Mettouchi ( , 2018 Mettouchi ( [2011]] ), unless preceded by a preposition, only some nominals, those inside the prosodic group of the verb, may be attributed a grammatical role. Nominals belonging to the sentence, but situated before or after the prosodic boundaries of the prosodic group of the verb can be coreferent to a bound pronoun that has a given grammatical or semantic role, but they do not, either through morphology or construction, encode such roles. Their function is more centrally in the domain of information structure and referent activation, as mentioned in the preceding part (see  METTOUCHI, 2015 METTOUCHI, , 2018 METTOUCHI, [2011]] ) for extended and commented examples from my corpus).
Bound pronouns and their roles While nominals are most of the time absent, bound pronouns are noticeable and frequent in Kabyle. The language has several pronominal paradigms  (METTOUCHI, 2017, p. 10-11). Among those, some are hosted by the verb: the subject affix, the absolutive clitic, and the dative (indirectly affected argument) clitic. Subject pronouns are affixes (their position relative to the verb is fixed), and only appear with verbs; dative pronouns are clitics (they undergo climbing in contexts of negation, relativization, or irrealis mood). This is also true for absolutive pronouns, which, additionally, are also hosted by some non-verbal predicates (they are their sole argument).
Coreferent nominal As is the case for all bound pronouns in Kabyle, the referent of absolutive pronouns can be expanded by a coreferent nominal. Whereas pronouns come in various paradigms, nouns must be either in the absolute or in the annexed state. This binary morphological marking, covert in the case of borrowings and for some classes of nouns with a special phonological structure, is marked differently depending on the gender and the number of the noun (table 1). The state distinction plays a structural role in the language. It is the backbone of the whole grammatical system of Kabyle and is functional at the level of the phrase as well as at the level of the clause and the sentence (METTOUCHI; FRAJZYNGIER, 2013; METTOUCHI, 2014) In Kabyle, the function of the annexed state is to "provide the value (in the logical sense) for the variable of the function grammaticalized in a preceding constituent" 9 (METTOUCHI; FRAJZYNGIER, 2013, p.
With the advance of new technologies and emergence of the concept of the smart city, there has been a dramatic increase in available information. Water distribution systems (WDSs) in which databases can be updated every few minutes are no exception. Suitable techniques to evaluate available information and produce optimized responses are necessary for planning, operation, and management. This can help identify critical characteristics, such as leakage patterns, pipes to be replaced, and other features. This paper presents a clustering method based on self-organizing maps coupled with k-means algorithms to achieve groups that can be easily labeled and used for WDS decision-making. Three case-studies are presented, namely a classification of Brazilian cities in terms of their water utilities; district metered area creation to improve pressure control; and transient pressure signal analysis to identify burst pipes.
Suitably handled, this new wealth will create an ideal scenario for economic growth with a higher quality of life for citizens and less damage to the environment  (Kramers et al. , 2014). To achieve this goal, it is necessary to suitably handle the available information about the systems, which is based on a high level of data acquisition, and thus create large databases. In water distribution systems (WDSs), these databases can integrate such variables as flow, pressure, water demand, pipe characteristics, water quality, climatic parameters, maintenance history, etc. Due to the size of the databases, computational tools are necessary to quickly identify problems and propose solutions. Dividing the available information into clusters is a mechanism that enables identifying patterns and the main features in databases.
Among several other clustering techniques, self-organizing maps (SOMs),  Kohonen (1982) , based on the theory of neural networks (NNs), have gained space in environmental resource research. Kohonen et al. (2000)  highlights the use of SOMs as a clustering tool for database operation. Izquierdo et al. (2016)  uses SOMs for early data labeling for the application of classification tools. Kalteh et al. (2008)  highlight its extensive use in water resources problems. More specifically, in WDSs, SOMs have been widely used for water quality analysis  (Blokker et al. , 2016) , optimal design  (Norouzi and Rakhshandehroo, 2011) , and pattern analysis  (Laspidou et al. , 2015) , among other uses. The application of data clustering in WDSs can help planning, operation, and management of these systems, since it is possible to identify anomalies, develop strategic operations for distribution, and evaluate the system through suitable indicators.
Considering the large extension of systems and their complex interconnections, the process to divide WDSs into DMAs can be a difficult task and result in poor scenarios when partitions are developed without appropriate tools. The use of NNs and machine learning tools enables pattern extraction and classification of large databases. However, the number of resulting groups is usually too large. Classification methodologies for planning, operation, and management often require a small number of groups to ease and accelerate decision-making. SOMs are unable to create this reduced predefined number of groups, which is a drawback for their application in these cases. When the number of groups can be predefined, the use of classical clustering methods, such as k-means algorithms, has been widely explored, since these methodologies can be faster than NN approaches.
Case study description The Brazilian National System of Sanitation Information (SNIS) is a large database on sanitation service performance. It is an important tool for planning public policies and management of the investments made. However, the large volume of information makes an assessment of the necessary improvements difficult. To improve the global quality of water services, the investment fields were divided and three major groups of indicators (namely: water quality, operational efficiency and economic performance) were created, each containing a set of relevant indicators for the global system quality evaluation described in Table  1. Although the fulfillment of SNIS is mandatory for water utilities in Brazil, some cities omitted information or presented data with errors, compromising the evaluation of all Brazilian cities.
Results and discussion The first clustering level, performed with an SOM, has the architecture defined by an optimal analysis of the computational index presented in equation (  5 ). The variation in the number of neurons decreases significantly the quantization error because the larger the mesh, the smaller is the distance between the winning neuron and the datum. The size of the mesh is closely linked to the processing time, which increases with the number of neurons. The SOM is computed by varying the number of neurons from 9 to 900 and the size of the neighborhood from 1 to 5 neurons. The architectural test and performance evaluation were developed in Matlab. Figure  3a , presents the computational index for the three indicator groups, highlighting the optimal number of neurons. Taking these values for water quality: SOMs with 144 neurons (operational data), 169 neurons (economic data), and 81 neurons were implemented.
Results and discussion To this purpose, topological features (nodal demand and elevation) and the spatial position of the node are used as input data for SOM. The study is applied on the C-Town network, a benchmarking case study used in many WDS analyses, such as for this study. To apply the hybrid SOM+k-means method, a database with the topology features of the nodes was created. The position of the node in the space, corresponding to the pair (x,y) coordinates, nodal demand, and elevation, formed the input matrix for SOM. In this case, the input matrix was also normalized by the zscore function, but the position data (x,y) was weighted after the normalization to increase the significance of this data in the clustering. This is important because for DMA creation, the neighborhood relationship of the nodes is paramount.
The continuity law is applied to a generic node and MOC positive and negative lines are used to calculate flow in convergent and divergent pipes respectively  (Almeida and Koelle, 1992). To model a pipe burst, a sudden leakage flow is added to the nodal demand. To create a more realistic scenario, the model proposed by Van Zyl   The main objective of applying the hybrid SOM+k-means algorithm is to identify patterns within the set of monitored pressures in previously defined nodes. For each leakage scenario, the pressure signal of the four monitored nodes was collected during a period of 60 s and concatenated for classification with the proposed method. For this application, the data is normalized using the z-score function, which, in this case, has a salient feature because maintaining the signal shape is very important for a good segregation of transient pressure.
CONCLUSIONS This paper presents a clustering tool based on SOMs coupled with a k-means algorithm to achieve a predefined number of groups, taking into account the complex relationship among features. In general, clustering data is a hard task, since the size of the databases and unclear relationships among their variables can hide potential clusters, thus forcing the application of robust computational tools. SOMs find patterns and project high dimensional databases in (mainly) two-dimensional maps. However, for certain applications, those maps exhibit level of granularity that is too fine. In other words, they produce a detailed description that is not directly useful in some decisionmaking processes and so some other procedure may be required. In this paper, we propose the hybrid use of SOMs as a preprocessing technique.
Firstly, an SOM produces a feature map with the optimal architecture reached by the minimization of the efficiency index. Working on an SOM-based dissimilarity matrix, a k-means algorithm then coarsens the map to produce a smaller number of groups that is more suitable for various decision-making processes. Three case studies for different problems of WDSs are presented. For the WDS indicator case study, some cities had missing or incorrect information (which could generate inconsistencies in clustering and so were discarded), showing the importance of pre-processing to obtain a reliable database. With the database of clean data, the method clearly separated the best and worst cities into different groups, which is very helpful for governmental investment policies. In addition, it is interesting to observe that the size of the city does not appear to influence WDS quality.
The method also presented good results in the DMA creation case by creating groups with similar elevation, which is a major characteristic for pressure management. The process of finding optimal entrance pressures demonstrates the importance of DMA creation due to pressure regularization. After this process, it is observed that the groups created by the hybrid method can also be differentiated by the pressure uniformity indicator, which can be used in leakage problems (since leaks are directly related to the operational pressure). As shown in this work, management, operation, and planning of WDSs can be improved if the available data is suitably used. Moreover, the larger the database, the harder the task of applying this information from a practical point of view. The good results obtained with these three case studies (referring to planning, operation, and management of WDSs) suggests that the hybrid method proposed can also be useful to handle problems in other contexts -especially in environmental fields.
We investigate time and frequency dissemination using software-defined radio processing of signals acquired from a low-frequency emitter using a sound card. We use the resulting propagation time measurements for investigating some ionosphere physics and its interaction with cosmic ray flux. Rather than using the amplitude of the transmitted signal as classically considered, we here focus on a precise time of flight measurement by demodulating the spectrum spreading phase modulation added to the DCF77 amplitude modulation. Plain Language Summary Software-defined radio aims at reducing to its minimum the hardware setup needed to process radio frequency signals and relies on software processing for demodulation: here we demonstrate the precise measurement of an atomic clock-locked emitter, propagating at very low frequency, using only a general purpose computer sound card.
digital converters (ADC) do not exhibit the sampling rate-typically a few MHz-needed to sample radio frequency signals: shifting the signal under investigation from its carrier frequency to baseband, close to 0 Hz, also allows for filtering strong interference sources and prevents saturating the sampling stage with a wanted signal below the ADC resolution. VLF (very low frequency, 3-30 kHz) and LF (30-300 kHz) allow implementing true SDR receivers: meeting Nyquist criteria of a sampling rate at least twice the targeted signal frequency range, recording DCF77 only requires an ADC with at least 150 ksamples/s sampling rate, a requirement met by most current sound cards sampling at 192 ksamples/s. Alternatively, we have successfully used a Digital Video Broadcast-Terrestrial (DVB-T) receiver fitted with a Realtek RTL2832U analog to digital converter sending data on a Universal Serial Bus, as implemented with the Osmosdr GNURadio source, after removing the radio frequency front end: in such a configuration, the in-phase (I) and quadrature (Q) inputs of the RTL2832U are, respectively, connected to the DCF77 and GPS 1-PPS outputs, the latter as reference.
The initial prototyping steps have been performed using GNURadio, a software framework designed to help SDR enthusiasts prototype digital signal processing functionalities yet provide real-time signal processing and visualization, as opposed to postprocessing using Matlab or its open source implementation, GNU/Octave. Phase detection and automated analysis over long durations will be performed with the latter software. Transposing from radio frequency band to baseband is such a common SDR processing task that it is implemented as an optimized processing block in GNURadio (Figure  4 ): the Xlating FIR Filter. The time t dependent signal s(t) received at the antenna exhibits a signal of interest modulated close to a carrier f c , while recovering the property of the signal requires getting rid of the carrier: demodulating requires reproducing a local copy of f c so that s(t)×exp(j2𝜋f c t) (1) shifts the incoming signal to baseband (here j 2 =-1).
Once the signal is shifted to baseband, the whole bandwidth, given by the initial sampling rate f s , is no longer needed since the signal is band limited: decimating, that is, taking one in every N samples, reduces the bandwidth by a factor of N, easing processing steps since the data rate has been reduced. However, decimating brings all signals in the initial frequency band of [-f s ∕2;+f s ∕2] to the new frequency band [-f s ∕(2N); +f s ∕(2N) ] by aliasing: low-pass filtering the frequency transposed signal prior to decimation is needed to get rid of these aliasing images, hence the inclusion of the Finite Impulse Response (FIR) filter in the GNURadio processing block. We now have a signal at baseband whose information content, lying in the amplitude and phase, must be decoded: such task will be performed solely by software processing.
From this offset, a local oscillator signal is generated using a time signal synthesized with steps equal to the inverse of the sampling rate, and a dot product simulates the multiplier component that would be used otherwise for frequency transposition. Having removed the coarse frequency offset, a linear fit on the phase removes the residual linear trend of the phase, also known as frequency (since the derivate of the phase is the instantaneous frequency). A precomputed pseudorandom code sequence is loaded and resampled at the same rate as the data recorded by the sound card. Following all these steps, the cross correlation between the pseudorandom sequence and the phase whose linear drift has been removed must exhibit a sharp peak once every second, when both patterns match. The cross correlation between the detrended phase and the pseudorandom sequence is computed, having previously removed the mean value of each signal to prevent a triangular baseline variation due to the integral over a constant offset: the cross correlation exhibits maxima every time the pseudorandom pattern is met in the phase of the recorded signal, as seen in Figures  7b  and 7d.
Allan deviation of the time offset between GPS 1-PPS and DCF77 (blue), and of a 32768 Hz tuning fork oscillator as classically found in wrist watches (red). The intersection of the two curves provides the time constant of the composite clock in which the DCF77 signal could be fed back to the tuning for oscillator to correct for long-term drift of the latter. The green curve exhibits the Allan deviation of the spring and summer data set, starting 1 April, when the ionosphere has stabilized during daytime, improving the time transfer stability.
Conclusion Software-defined radio and digital signal processing are used to analyze a high stability time and frequency transfer signal emitted at very low frequency by the German DCF77 emitter. Since the propagation of this signal is dependent on ionospheric conditions and especially the altitude of the layer with the electron density whose plasma frequency matches the radio frequency wave frequency, the time of flight is representative of the ionosphere altitude variation. Daily and seasonal variations are readily observed, thanks to the improved timing capability of the pseudorandom phase modulation added to the coarse amplitude modulation used for time transfer. The temperature dependence of the local oscillator of the receiver is also observed with this setup, which solely consists of an antenna, impedance matching circuit, and personal computer sound card.
These last fine-tuning steps must be repeated for each new record since the local oscillator frequency, clocking the sound card, fluctuates over time with environment (Figure  6 ). Having recovered a fine estimate of the received signal phase, we aim at extracting the pseudorandom phase sequence imprinted on the carrier. The bit-sequence generated by the polynomial was computed and stored in a lfsr. dat file as described in section 3, with a rate of 1 sample/state. The sampling rate resulting from the decimation was selected to have a number of samples of the phase close to an integer number of samples of the phase encoding: at 120 periods/phase state, the number np of samples is 192, 000∕59×(120∕77, 500)= 5. 04, close enough to 5 for the 512 sample long pseudorandom code to be easily resampled to match the current sampling rate: each bit is copied enough time for the sampling rates to match, resulting in the longlfsr vector.
Acknowledgments Andreas Bauch  (PTB, Germany) prompted this investigation with his course on time transfer at the European Frequency and  Time Seminar  (efts. eu). Franck Lardet-Vieudrin (FEMTO-ST, France)  provided support in designing and understanding the operating principle of the short antenna. François Vernotte  ( Besançon Observatory, France ) provided the explanation on the conversion of time intervals to normalized quantities for Allan deviation analysis. Eric Meyer  ( Besançon Observatory, France ) prompted the investigation on the cross-polarization time delay measurement. This work was partly supported by the  Programmes d'Investissements d'Avenir (PIA) FirstTF  and Oscillator IMP grants from  Agence Nationale de la Recherche (ANR) , in addition to a PEPS grant by the  French Centre National de la Recherche Scientifique (CNRS).
The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (2 9 ), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found. Directed protein evolution is a relatively tedious and time-consuming endeavour. Originally starting from purely random mutagenesis approaches 1,2 , protein engineering has advanced to a more and more information driven effort (site directed, structure based)  [3] [4] [5] [6] [7].
More recently new methods for the detection of solenoids domains in protein structures were proposed  36, 37. Cosić has developed the most known approach using DSP and called Resonant Recognition Model (RRM). Digital Signal Processing techniques have helped analyse protein interactions  26, 38  and made biological functionalities calculable. In these approaches, protein residues are first converted into numerical sequences using one of the available AAindex from this database  39, 40  , representing a biochemical property or physicochemical parameter for each amino acid. These numerical sequences are then processed by means of Discrete Fourier Transform (DFT) to present the biological characteristics of the proteins in the form of Informational Spectrum Method (ISM)  41. ISM procedure has been used to investigate principal arrangement in Calcium binding protein  25  and Influenza viruses  42, 43.
Although CASTing and iterative saturation mutagenesis (ISM) have since been improved requiring notably less screening  52  , we were interested in exploring the possibility of applying machine learning for efficient directed evolution of enantioselectivity using the same model reaction. To develop a really useful sequence-based statistical predictor for a biological system, one should observe the following five steps 53 : (i) how to construct or select a valid benchmark dataset to train and test the predictor; (ii) how to formulate the biological sequence samples with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted; (iii) how to introduce or develop a powerful algorithm (or engine) to operate the prediction; (iv) how to properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (v) and as far as possible, how to establish a user-friendly web-server for the predictor that is accessible to the public.