dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance of local alignment-based spectral kernels Table  1  summarizes the performance achieved by the local alignment based kernels for family-level classification implemented with spectral clustering. We measure the performance of BLASTP kernel(I), PSI-BLAST kernel(II), OrthoMCL Neighborhood Mismatch (OMCL NM) kernel(III) and OrthoMCL Mismatch Profile (OMCL MP) kernel(IV) to classify the multi-domain protein families of our dataset with mean ROC and mean ROC50 scores. These results show that OMCL MP kernel(IV) performs best over all other methods indicating the influence of profiles in homolog detection. All the modified local alignment kernels outperforms simple score based kernels in this experiment. As an illustration, the distribution of ROC50 scores for all local alignment-based kernels is shown in Figure  1.
Performance of combined spectral kernels In order to investigate the performance of our spectral kernels over simple alignment kernels, we combine all modified local alignment kernels using normal product. Combining PSI{BLAST with OMCL NM(VI) and OMCL MP (VIII) kernels provide respectively ROC values 0:757 and 0:773 in Table  2 , which is superior to the values 0:738 and 0:752 obtained by combining BLASTP kernel respectively with OMCL NM (V) and OMCL MP (VIsI) kernels. PSI{BLAST with OMCL MP kernel (VIII) outperforms all other methods with the highest ROC50 score of 0:773. Figure  2  illustrates the combined kernel performances of ROC50 distribution for the unlabelled protein family classification. The basic BLASTP (I) and PSI-BLAST (II) kernels cannot successfully perform in the absence of sufficient positive training data for a huge unlabelled protein database  [7].
Comparative performance evaluation We evaluate the clustering solutions of our proposed kernels comparatively with those of the already-existing linear  [44] , mismatch  [22]  and string  [28]  kernels. We experiment those mismatch  [22]  and string  [28]  kernels over the BLASTP, PSI-BLAST and OMCL matrices to obtain ROC50 scores provided by those kernels. In Table  6 , the ROC50 scores provided by those existing kernels are shown. The ROC50 scores of our proposed kernels in Tables 1, 2, 3, 4 show superior efficiency with higher ROC50 scores. Similarly, to experiment with a large dataset, we run all our proposed kernels as-well-as the state-ofthe-art linear  [44] , string  [28]  and mismatch  [22]  kernels on SVM over the existing dataset with 54 families from SCOP version 1. 59  [23]. We experiment the existing linear  [44]  and string  [28]  kernels over this dataset and compare it with existing results of Spectrum Mismatch kernel  [22].
Conclusions The homologous protein family detection tool within Hemiascomycete yeast complete genomes are appreciated in genomics to detect the conservation of function. Therefore, we propose a computational approach for computing local alignment based Mercer kernels utilizing Markov similarity to reduce ''recent'' paralog effects. Introducing profile mismatching and neighborhood feature vectors in combined Mercer kernels for spectral clustering, effectively escalates remote homolgy detection from unlabeled protein sequences database. We experiment the corrections by the modified symmetry based proximity norm producing improved clusters with reduced outliers/singletons and selecting more biologically significant domains for multidomain proteins. Our position specific scoring kernel combined with the modified symmetry based corrections, achieves state-ofthe-art prediction performance in the context of unsupervised homology detection.
The Dome C (Concordia) station in Antarctica (75806'S, 123821'E, 3233 m above mean sea level) has a unique opportunity to test the quality of remote-sensing measurements and meteorological analyses because it is situated well inside the Eastern Antarctic Plateau and is less affected by local phenomena. Measurements of tropospheric temperature and water vapour (H 2 O) together with the integrated water vapour (IWV) performed in 2010 are statistically analysed to assess their quality and to study the yearly correlation between temperature and H 2 O over the entire troposphere. The statistical tools include yearly evolution, seasonally-averaged mean and bias, standard deviation and linear Pearson correlation. The datasets are made of measurements from the ground-based microwave radiometer H 2 O Antarctica Microwave Stratospheric and Tropospheric Radiometer (HAMSTRAD), radiosonde, in situ sensors, the space-borne infrared sensors Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A platform and the Atmospheric InfraRed Sounder (AIRS) on the Aqua platform, and the analyses from the European Centre for Medium-Range Weather Forecast (ECMWF).
Despite some obvious biases within all these datasets, our study shows that temperature and IWV are generally measured with high quality whilst H 2 O measurement quality is slightly worse. The AIRS and IASI measurements do not have the vertical resolution to correctly probe the lowermost troposphere, whilst HAMSTRAD loses sensitivity in the upper troposphere-lower stratosphere. Within the entire troposphere over the whole year, it is found that the time evolution of temperature and H 2 O is highly correlated (. 0. 8). This suggests that, in addition to the variability of solar radiation producing an obvious diurnal cycle in the planetary boundary layer in summer and an obvious seasonal cycle over the year, the H 2 O and temperature intra-seasonal variabilities are affected by the same processes, e.
Thus, when comparing with space-borne measurements (by considering measurements within a circle of about 100 km or a box 28 x28 centred at the Dome C station), retrievals are less affected by these surface inhomogeneities, and the associated statistics (mean, standard deviation, etc. ) are meaningful. Finally, any instrument has its own strengths and weaknesses, and it is also a key issue of this manuscript to identify these terms when performing comparisons. The aim of the present paper is twofold: 1) to assess the quality of the different H 2 O and temperature datasets available over one year, namely 2010, above the station of Dome C by performing a rigorous statistical analysis, and 2) to study the temperature vs H 2 O correlation over the entire troposphere and discuss the processes that produce the observed variability and correlations.
To achieve this goal, we use standard statistical tools, such as mean, bias, standard deviation and linear Pearson correlation coefficient, calculated over three different seasons: summer, autumn/ spring and winter; although we separated autumn and spring when considering temperature fields in the upper troposphere-lower stratosphere. Unfortunately, all the datasets are not available at the same local time, that is particularly important in the planetary boundary layer in summer due to the strong diurnal cycle in temperature and H 2 O  (Ricaud et al. 2012). Consequently, we have decided to consider, as a reference, the measurements from one specific instrument, namely the ground-based microwave radiometer H 2 O Antarctica Microwave  Stratospheric and Tropospheric Radiometer (HAMSTRAD). This radiometer is able to sample the whole 24-hour diurnal cycle with a seven minute integration time from mid-January 2010 to end of December 2010, and from 0 m to 10 km above surface, with a vertical resolution from , 30 m in the planetary boundary layer to , 500 m in the upper troposphere-lower stratosphere  (Ricaud et al.
2010a (Ricaud et al. , 2013)). From previous studies  (Ricaud et al. 2010b (Ricaud et al. , 2012 (Ricaud et al. , 2013)) , we already know the strengths and weaknesses of this instrument that will be discussed later. The other datasets used in our study are: radiosondes, in situ sensors, the two IR space-borne sensors: Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A platform and Atmospheric InfraRed Sounder (AIRS) on the Aqua platform, and, finally, the analyses from the European Centre for Medium-Range Weather Forecast (ECMWF). The article is structured as follow. We present the different instruments involved in this study, namely HAMSTRAD, radiosondes, in situ sensors, space-borne sensors (IASI and AIRS) and ECMWF analyses. The statistical analyses in terms of yearly evolution, mean and bias, standard deviation and correlation are shown and discussed for tropospheric temperature, integrated water vapour (IWV) and tropospheric H 2 O.
(2010a). From February-June 2008, HAMSTRAD was firstly deployed at Pic du Midi station (42856'N, 0808'E, 2877 m a. m. s. l. , France) for a first validation of water vapour measurements  (Ricaud et al. 2010b). Then, the radiometer was sent to Dome C and installed outdoors in January 2009 for 12 days. Finally, after 16 January 2010, the instrument was deployed indoors inside a dedicated, equipped and heated container protected by a shield. It has been running automatically since then. The noise diode attached to the 183-GHz channels enabling automated calibration failed in June 2011, and was replaced in February 2012. Consequently, H 2 O measurements are not available over this period. This means the longest period available so far for a statistical comparison of H 2 O and temperature with other datasets covers the whole year of 2010.
Statistical analyses of tropospheric temperature This section is dedicated to the statistical analysis of temperature. The different datasets used in our study have been seasonally averaged as follow: summer (January, February, November and December), autumn/spring (March, April, September and October) and winter (May, June, July and August). For one specific study (temperature evolution in the upper troposphere-lower stratosphere), we have separated the autumn (March and April) and the spring (September and October) seasons. We first present the yearly evolution of temperature as detected by the different sensors and analyses. Then, we show the mean seasonally-averaged profiles and biases, followed by the seasonally-averaged standard deviations and seasonallyaveraged correlations. Note that height, within the entire manuscript, is defined as above the Dome C surface (unless specified).
Yearly evolution Figure  1  shows the yearly evolution of the temperature as measured by the different instruments (HAMSTRAD, radiosondes, IASI and AIRS in ascending node) and analysed by ECMWF from January-December 2010. We can distinguish two main periods: 1) summertime  From the lowermost to middle troposphere, the temperature fields for all the datasets are symmetrical with respect to the winter period (July-August), whilst the fields are asymmetrical in the upper troposphere-lower stratosphere, with spring (, 210 K) much colder than autumn (. 210 K). This is related to the evolution of the polar vortex that builds up in the beginning of winter and collapses when air starts warming up in spring, see e. g. Ricaud et al. (2005). In general, all the datasets behave consistently to each other, except for two main differences: 1) considering the HAMSTRAD dataset, the mid-to-upper troposphere is much warmer by about 10 K on average and the tropopause height is much lower by about 1-3 km than in all the other datasets, and 2) the lowermost troposphere as measured by AIRS in autumn/spring and winter is much colder by about 10-20 K than in all the other datasets.
Another important point is the absence of IASI measurements above Dome C, mainly after 14 September 2010. On that particular date, the retrieval software developed at EUMETSAT was updated to v5. 0. 6 and pixels over icy surfaces were systematically but erroneously flagged as cloud-contaminated pixels, and consequently, not processed. Radiosonde and ECMWF temperature fields are very similar due to the assimilation of radiosondes launched at Dome C into the ECMWF analysis and forecast system.
Mean and bias Figure  2  shows the seasonally-averaged temperature profiles as measured by the different instruments and analysed by ECMWF together with their absolute biases against the HAMSTRAD dataset. Biases against HAMSTRAD are calculated in temporal coincidence within a 60-min window, in summer, autumn/spring and winter. We have performed a sensitivity test considering a 30-min window instead of a 60-min window. The statistics remain unchanged with one major exception: the correlation between HAMSTRAD and in situ datasets in the planetary boundary layer in summer that is close to 1. 0 in a 30-min window instead of 0. 8-0. 95 in a 60-min window. Indeed, the intense diurnal variation of temperature in the planetary boundary layer in summer (see  Ricaud et al. 2012) , well depicted by the high temporal resolution of in situ and HAMSTRAD data, slightly affects the statistics in this case alone.
From Fig. 1 , we noticed that, in the upper troposphere-lower stratosphere, the temperature fields were not similar in autumn and in spring. Consequently, we have separated in Fig. 3  the seasonally-averaged temperature profiles from the different datasets in autumn and spring, together with the biases of all the datasets against the HAMSTRAD dataset. In the lowermost troposphere, whatever the season considered, HAMSTRAD, in situ, radiosonde and ECMWF datasets are very consistent with each other showing a strong temperature gradient (from 235-243 K in summer and from 211-232 K in winter), from 10 to , 900 m. Biases with respect to the HAMSTRAD dataset are from -1 to 1 K ( ± 0. 5%) in summer and from -6 to 1 K (-2 to 0. 5%) In the free troposphere (2-6 km above surface), all the datasets show a systematic 5-7 K (2.
5-3%) positive bias with respect to the HAMSTRAD dataset, maximum between 4 and 4. 5 km in summer, 3-3. 5 km in autumn/ spring and 2. 5-3 km in winter. This means that the radiosonde, AIRS, IASI and ECMWF profiles are all consistent with each other to within ± 1 K (1%). In the upper troposphere-lower stratosphere (height. 6 km), the HAMSTRAD profile shows the minimum of temperature, characterizing the tropopause, at , 5km throughout the year, from 206 K (winter) to 215 K (summer), while the other instruments present a minimum at 5. 5-6 km (217 K) in summer and above 10 km in winter. In autumn (Fig. 3 ), the minimum is at , 5 km for all the datasets whilst, in spring, it is at , 5 km in the HAMSTRAD dataset and above 10 km in all the other datasets. Above the tropopause, all the datasets (except IASI in summer and spring) present a systematic 9-14 K (3.
5-6%) negative bias with respect to the HAMSTRAD measurements. When compared to all the other datasets except IASI, the shape of the HAMSTRAD profiles in summer, spring and winter seems unrealistic, with a strong gradient from the tropopause to the stratosphere whilst the HAMSTRAD profiles appear to be consistent with IASI in summer. This again confirms the previous studies stating that HAMSTRAD loses sensitivity in the upper troposphere-lower stratosphere. Above 6 km, radiosonde, AIRS and ECMWF agree to within 1-2 K.
Standard deviation Figure  4  shows the seasonally-averaged absolute standard deviations as calculated for the different instruments and analysed by ECMWF in summer, autumn/spring and winter. In the lowermost troposphere, all the datasets (except IASI) show a strong gradient from the surface to 400 m, weak in summer from about 7. 5 K at the surface to 5 K (3-2%) at 400 m and strong in winter from approximately 9. 5 K at the surface to 4 K (3. 8-1. 6%) at 400 m. Note that, in conjunction with the maximum observed in the AIRS profile at , 1 km and discussed in the previous section, a local minimum appears between 500 and 1000 m, ranging from 2. 5 K (1%) in autumn/spring to 4 K (1. 6%) in winter. Probably due to the IASI low sensitivity in the planetary boundary layer, the IASI standard deviation does not present any gradient in the lowermost troposphere but also within the whole troposphere in summer.
This is unrealistic in summer (2. 5 K instead of 4-6 K for all the other datasets) although in winter and, to a lesser extent, in autumn/spring, the IASI standard deviation is consistent with all the other datasets (4-6 K). In the free troposphere (1-5 km), all the datasets have a globally constant standard deviation, ranging from 4-5. 5 K (1. 6-2. 2%) in summer and from 3-4 K (1. 2-1. 6%) in autumn/spring, except HAMSTRAD exhibiting an obvious maximum around 2-2. 5 km approximately 0. 5-1 K (0. 2-0. 4%) greater than all the other datasets. In the upper troposphere-lower stratosphere, the standard deviation of all the databases are all increasing with height, whatever the season considered, with values of 6-8 K around 8 km in summer and winter, up to 12 K in autumn/spring probably due to the breaking up of the vortex in spring.
Correlation Figure  5  shows the radiosonde, IASI, AIRS, in situ and ECMWF dataset correlation with respect to the HAMSTRAD dataset, calculated in temporal coincidence within a 60 min window. In the lowermost troposphere, whatever the season considered, the radiosonde, in situ, AIRS (in summer) and ECMWF datasets show a very high correlation. 0. 8, greater than 0. 9 in summer, with respect to the HAMSTRAD dataset. The correlation is very weak for IASI (0. 2 in summer and 0. 5 in winter and autumn/ spring). From 0-2 km above surface, the IR space-borne instrument datasets (AIRS and IASI) give correlations systematically less than all the other datasets. This reinforces the point discussed in the previous section, namely that both the vertical resolution and the loss of sensitivity of IASI in the planetary boundary layer prevent the giving of meaningful information on temperature in the lowermost troposphere.
In the free troposphere, above 2 km, the correlation between the different datasets and the HAMSTRAD dataset is also high (. 0. 8) except for IASI in winter (0. 3-0. 7). In summer, the IASI correlation is. 0. 8 between 1. 5 and 4 km, decreasing down to 0. 4 at 5 km. The weak IASI correlation in winter (0. 3) is rather intriguing and cannot be explained at the present time unless retrievals are greatly affected by an unsuitable surface emissivity used in the EUMETSAT retrieval software during this particular period when no solar radiation reaches the surface. A few months earlier, namely in autumn/spring and summer, the correlation coefficient is rather high (. 0. 8) in the free troposphere, thus IASI gives meaningful information on mid-tropospheric temperature (see previous sections). In the upper troposphere-lower stratosphere, correlations with HAMSTRAD are systematically lower than in the troposphere but nevertheless not negligible (0.
6-0. 8), with a local minimum around 6 km in summer (0. 7 for the AIRS, 0. 6 for the radiosonde and ECMWF datasets and 0. 4 for IASI). Although HAMSTRAD loses sensitivity in this layer  (Ricaud et al. 2013) , it can nevertheless give meaningful information on temperature relative to, for example, time evolution and/or climatology. From surface to 10 km, ECMWF and radiosonde correlations are very similar in amplitude and shape, again underlining the fact that radiosondes launched at Dome C are used in the ECMWF analysis and forecast system. This underlines the great potential of HAMSTRAD measurements to validate both meteorological analyses and satellite measurements in such a remote place where very few independent data are available.
Statistical analyses of Integrated Water Vapour (IWV) Yearly evolution Figure  6  shows the temporal evolution of IWV above Dome C in 2010 as measured by HAMSTRAD, radiosonde, AIRS (ascending and descending nodes), IASI, and analysed by ECMWF. As expected from the time evolution of temperature (Fig. 1 ), a dry season is observed from May-August with values ranging from 0. 05-0. 6 kg m -2 and a wet (or less dry) season appears from November-February with values ranging between 0. 4 and 1. 5 kg m -2 ,s h o w i n g some intense spikes in February and December with values. 2. 1 kg m -2. We note the great consistency within all the datasets showing high (respectively weak) variabilities in summer (respectively winter) within a few days in agreement with  Tomasi et al. (2012).
Bias Figure  7  shows the IWV absolute biases of all the datasets with respect to the HAMSTRAD dataset selected within a 60-min time coincidence window. A systematic negative bias is observed in winter of about -0. 03 ± 0. 01 kg m -2 (-15 ± 5%) from May-August whilst, in summer, the negative bias reaches -0. 1 ± 0. 05 kg m -2 (-10 ± 5%). The 4-15% dry bias of the radiosondes during polar days is consistent with the 10-15% dry biases reported in  Tomasi et al. (2011 Tomasi et al. ( , 2012)). We can note, in summer, the ECMWF bias is close to zero or slightly positive (0. 06 kg m -2 ). The IASI strong negative biases (, -0. 3 kg m -2 ) detected in October and November can certainly be attributed to the changes in the retrieval software at EUMETSAT that occurred on 14 September. The AIRS (ascending and descending nodes) biases in summer are systematically negative and much lower from -0.
Standard deviation The monthly-averaged absolute standard deviations of IWV from HAMSTRAD, radiosonde, AIRS (ascending and descending nodes), IASI and ECMWF datasets are presented in Fig. 8 , showing a minimum in winter of , 0. 10 kg m -2 (35 ± 10%) and a maximum in summer of 0. 15-0. 55 kg m -2 (40 ± 10%). We can note a local maximum in February (0. 4 kg m -2 ) as detected in the radiosonde and ECMWF datasets that is not present in all the other datasets.
Correlation Figure  9  shows the IWV correlation of all the datasets calculated with respect to the HAMSTRAD dataset selected within a 60-min time coincidence window. On average, the correlation with HAMSTRAD is very high when considering the radiosondes (. 0. 9) and the ECMWF (. 0. 8) except in January (0. 6), high when considering AIRS ascending and descending nodes (. 0. 7) except in January (, 0. 0 and -0. 4, respectively), but lower when considering IASI in January, February, July, August and November (, 0. 6), and slightly higher (, 0. 7) in May, June, September and October.
Yearly evolution Figure  10  shows the temporal evolution of H 2 O above Dome C in 2010 as measured by HAMSTRAD, radiosonde, IASI, AIRS in ascending node and analysed by the ECMWF. As expected from the time evolution of IWV (Fig. 6 ), a wet season in summer followed by a dry season in winter is observed considering all the datasets with no obvious difference between autumn and spring patterns, consistent with  Tomasi et al. (2012). In summer, the amount of H 2 Oi s. 0. 1 g m -3 from the surface to a height of about 2 km for HAMSTRAD and AIRS, reaching , 3 km for radiosonde and ECMWF up to , 4 km in the IASI dataset. In winter, HAMSTRAD and AIRS tend to measure H 2 O amounts (, 0. 3 g m -3 ) much greater than all the H 2 O amounts from the other datasets (0. 06 g m -3 ). As for temperature, IASI H 2 O measurements are missing after 14 September because of a change in the analysis software developed at EUMETSAT: icy pixels were usually considered as cloudy pixels and consequently not processed.
16 g m -3 (HAMSTRAD) in winter. Note that in situ sensors always measure the driest atmosphere at 10 m (from 0. 18 g m -3 in summer to 0. 01 g m -3 in winter). In winter, the vertical profiles of all the datasets agree with each other to within 0. 05 g m -3 whilst, in summer, the profiles agree to  within 0. 02 g m -3. As already shown in previous studies  (Ricaud et al. 2013) , compared to all the other datasets, HAMSTRAD tends to measure a wetter atmosphere below , 2 km and drier above, whatever the season considered. Below 500 m, with the exception of HAMSTRAD (and IASI in summer), the absolute spread within all the datasets is very low in winter (0. 02 g m -3 ) and higher in summer (0. 05 g m -3 ), corresponding to a relative spread of , 20% whatever the season considered. Above 500 m, all the datasets are consistent with each other and show a decrease of the H 2 O amount.