dataesr/scientific-paragraphs-categorization huggingface dataset:
In band 8, we used two spectral windows to cover the velocity range -1350 km s -1 to 840 km s -1 around the CO(4-3) line, one spectral window to target the HCO + (5-4) line, and one spectral window to obtain the continuum at restframe 668. 4(±1. 7) µm. For the line observations, we typically chose a velocity bin of 5(±1) km s -1 , which is well suited for the detection of lines originating from small cloud ensembles (either in emission or in absorption in front of a background continuum). For the continuum observations, we chose coarser velocity bins of ∼20 up to ∼90 km s -1. To create the cubes, we reran the Common Astronomy Software Applications (CASA) reduction routines delivered by ESO. The calibration routines were run with CASA version 4. 3. 1, 4. 2. 2, and 4. 3. 1 for bands 3, 7, and 8, respectively.
The automated pipeline results were restored for bands 3 and 7. For band 8, the calibrations were fine tuned: the data from the antennas DA34 and DV02 were flagged due to high system temperatures caused by an error in the antenna position database. After a first basic flagging, the calibration pipeline computed the water vapor, system temperature, and antenna position calibration solutions and applied them to the measurement set (MS), that is, to the visibilities of all baselines, spectral windows, and target fields. It then extracted the pertinent spectral windows and continued with the masking of antenna-shadowed baselines and edge channels. The assignment of a flux model for the calibrator, and the computation of analytic expressions for the bandpass (phase and amplitude vs. frequency) and gain (phase and amplitude vs.
In all bands, the configuration of the antennas led to a subarcsecond beam. The beam was 0. 62 × 0. 56 at a position angle (PA) of 14 • in band 3, 0. 61 × 0. 47 at a PA of 53 • in band 7, and 0. 43 × 0. 36 at a PA of -1 • in band 8. At a common pixel scale of 0. 08 and at a common velocity resolution of 20 km s -1 , the noise level is at 0. 40 mJy/beam for CO(1-0), at 1. 1 mJy/beam for CO(3-2), and at 1. 2 mJy/beam for CO(4-3). For the examination of flux ratios, we produced a second set of datacubes, by convolving the data to a common beam of 0. 63 ×0. 63 using the routine ia. convolve2d. In this fully homogenized dataset the noise level is at 0. 41 mJy/beam, 1. 2 mJy/beam, and 1. 4 mJy/beam for CO(1-0), CO(3-2), and CO(4-3), respectively. To create the deepest possible cubes for each line, we also looked into the archive for additional CO line data from observations of 4C12.
50 as a calibrator for other programs. Indeed, the CO(1-0) line was observed with 38 antennas for 13. 6 mins as a calibrator for the program 2013. 1. 00976. S. We calibrated the measurement set using the delivered calibration pipeline with CASA 4. 7. 0, and then we merged the calibrated measurement set with ours using the routine concat in CASA 5. 4 to create a final cube with ∼40 mins of on-source exposure. As before, we used tclean to reconstruct this cube, which has a new (common) beam of 0. 81 ×0. 74 at a PA of -22 •. The common pixel scale of 0. 08 was selected for comparison purposes. This CO(1-0) cube reached noise levels of 0. 17 mJy at a spectral resolution of 100 km s -1. Likewise, the CO(3-2) line was observed with 27 antennas for 5. 8 mins as a calibrator for the program 2012. 1.
A Gaussian fit to the data provides the residual emission underlying the disk emission (Fig. 11 ), indicating a residual flux of 0. 94(±0. 12) Jy km s -1. This corresponds to a nuclear wind mass of 5. 3(±0. 7)×10 8 M. Despite the strong continuum and the high dynamic range (of order 1000; Fig. 4 ), the wind detection survives the test of altering pipeline parameter values (e. g. , first-and second-order continuum fit, different spectral window ranges for the continuum fit) in both datasets, that is, in our high-resolution and in the deep cubes. In previous work carried out by the team using Plateau de Bure (PbD) data  (Dasyra et al. 2014; Dasyra & Combes 2012)  this nuclear emission was not detected because the spectral window employed was not sufficient (the PdB data reached -1500 km s -1 , whereas the detection in the current ALMA data exceeds -1800 km s -1 ; Fig.
Conclusions We obtained ALMA data of the radio galaxy 4C12. 50 in order to determine the millimeter properties of its outflow, which was previously known to exist from large-and small-angularresolution observations at other wavelengths. We mapped the CO distribution, kinematics, and excitation with ALMA at a resolution of ∼0. 5 and found the following results. -The main gaseous disk is rather compact, extending to radii of ∼6 kpc in CO (1-0) and ∼2 kpc in CO (4-3). Some corotating CO emission is also seen in the secondary nucleus and in a bridge connecting the nuclei. -A shock-tracer, H 2 O, and a hard-ionization-field tracer, HCO + (5-4) are seen in the main nucleus. -Several extranuclear structures are seen. These include a prominent tidal tail south of the main nucleus with gas seen in negative and positive velocities.
Acknowledgements. This paper makes use of the  ALMA data ADS/JAO. ALMA 2013. 1. 00180. S, 2013. 1. 00976. S, 2012. 1. 00797. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada)  and  NSC and ASIAA (Taiwan)  and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. K. M. Dasyra  acknowledges financial support by the  Hellenic Foundation for Research and Innovation (HFRI)  and the  General Secretariat For Research and Technology , under grant number  1882. We would like to thank the referee,  B. Emonts , for detailed comments which led to a significant improvement of the paper.
In previous work by others, the quantization error from SOM output has been exploited as a qualitative neural network metric, either in comparison with vector quantization in variable neural network architectures used on the same image contents  (Kohonen, Nieminen, & Honkela, 2009)  or, more recently as a metric for dynamic precision scaling in neural network training  (Taras & Stuart, 2018). The Self-Organizing Map (a prototype is graphically represented here in Fig. 1  for illustration) may be described formally as a nonlinear, ordered, smooth mapping of high-dimensional input data onto the elements of a regular, low-dimensional array  (Kohonen, 2001). Assume that the set of input variables is definable as a real vector x, of n-dimension. With each element in the SOM array, we associate a parametric real vector m i , of n-dimension.
These local contrast changes were reliably captured by the variations in the SOM-QE (Fig. 2 ). (2) In the context of image-based environmental change detection; the same approach was applied to time series of satellite images of the Nevada Desert nearby Las Vegas City, USA. The original 25 images for the reference time period between 1984 and 2009 were taken from NASA's Landsat database. We fed these images into minimal pre-processing using a contrast normalization function to ensure equivalent image quality and then trained the SOM on the first image of the series. Image-by-image SOM Fig. 2. After training a SOM on the first image of a first MRI time series (20 images per series; only 12 from each clinical visit are displayed here for illustration) SOM analysis was performed on the other images in the series, taken from two clinical examinations of a patient's knee, before and after a minor accident.
SOM-QE versus RGB mean For RGB Mean calculations we used ImageJ, developed by the NIH https://imagej. nih. gov/ij/ The average grey (RGB) level, or RGB Mean, in each single image from a given series corresponds to the sum of all grey values (RGB) of image pixels divided by the total number of image pixels. For RGB colour images, the software converts each colour pixel (CP) to greyscale beforehand, using CP = 0. 299xR+0. 587xG+0. 114xB (4) In order to compare how well the SOM-QE from performs in comparison with non-learning single-image analysis of the RGB Mean, all the images from the different series were uploaded one by one to Image-J and the mean for each image was computed.
Acknowledgements The corresponding author of this article (BDL) expresses her gratitude to  Stephen Grossberg (SG)  for hosting her project in 1998, when BDL was a Fellow of the  Human Frontiers Science Program Organization (HFSPO) , and SG the founding Director of the  CAS/CNS at Boston University. We would like to thank the Guest Editor  Donald Wunsch  and two anonymous reviewers for their helpful comments and suggestions. Our Norwegian colleagues O. V. Solberg  and  O. K. Ekseth  provided further advice and encouragement, for which we are most grateful.
THERMOSS In a real sensors network, a telecontrol system must periodically (e. g. , every few minutes) acquire, store and validate data gathered by sensor measurements in order to achieve accurate monitoring of the whole network in real time. These values need to be validated before further use, in order to assure the reliability of the results obtained when using these data. The digitalization in the building sector (in this case residential), can help with better overview of the energy flows and targeting energy savings, but also for maintenance (predictive maintenance) and faults identification. THERMOSS database store everyday more than 150. 000 single data related to the sensors installed in demo sites across Europe. A relative data check tool for the analysis of incoming data and relative notifications have been implemented to improve data reliability and help in data handling.
We describe a tool, named ebwt2snp, designed to detect positional clusters and post-process them for assembly-free and reference-free SNPs detection directly on the eBWT of reads collection. Among several reference-free SNPs finding tools in the literature  [8, 11, 25, 26] , the state-of-the-art is represented by the well documented and maintained KisSNP and DiscoSnp suite  [8, 25, 27] , where DiscoSnp++  [26]  is the latest and best performing tool. In order to validate the accuracy of positional clustering for finding SNPs, we compared DiscoSnp++ sensitivity and precision to those of ebwt2snp by simulating a ground-truth set of SNPs and a read collection. We moreover performed experiments on a real human dataset in order to evaluate the performance of our tool in a more realistic scenario.
Results on reads simulated from human chromosomes show that, for example, using coverage 22× our tool is able to find 91% of all SNPs (vs 70% of DiscoSnp++) with an accuracy of 98% (vs 94% of DiscoSnp++). On real data, an approximate ground truth was computed from the raw reads set using a standard aligner-based pipeline. The sensitivity of DiscoSnp++ and ebwt2snp turn out to be similar against this ground-truth (with values ranging from 60 to 85%, depending on the filtering parameters), but, in general, ebwt2snp finds more high-covered SNPs not found by the other two approaches. A preliminary version of this paper appeared in  [28]  with limited experiments performed with a prototype tool. This version includes an extension of our strategy to diploid organisms, results on a real dataset, and a new pipeline to generate a.
Then, X follows a Poisson distribution with mean = m • r-k n (1 -ǫ) k. Theorem 3. 2 states that, if there exists a value k < r such that G[i + 1, i + k] appears only once in G (i. e. if the cluster of i is not ambiguous), then X of the b -a + 1 letters in ebwt[a, b] are read-copies of G[i]. The remaining (ba + 1) -X letters are noise intro- duced by suffixes that mistakenly end up inside gsa[a, b] = m • r -k n (1 -ǫ) k Pr(R[j] ∈ ebwt[a, b]) = r -k n (1 -ǫ) k due to sequencing errors. It is not hard to show that this noise is extremely small under the assumption that G is a uniform text; we are aware that this assumption-as well as those required by Theorem 3. 2-is not completely realistic, but we will experimentally show in "Experimental evaluation" section that also on real datasets our approach produces accurate results (as predicted by our simplified theoretical framework).
A pipeline for SNPs detection When the reads dataset contains variations (e. g. two allele of the same individual, or two or more distinct individuals, or different isoforms of the same gene in RNA-Seq data, or different reads covering the same genome fragment in a sequencing process, etc. ), the eBWT positional clustering described in "eBWT positional clustering" subsection can be used to detect, directly from the raw reads (hence, without assembly and without the need of a reference genome), positions G[i] exhibiting possibly different values, but followed by the same context: they will be in a cluster delimited by LCP minima and containing possibly different letters (corresponding to the read copies of the variants of G[i] in the reads set). We now describe how to use this theoretical framework to discover SNPs just scanning eBWT, LCP and GSA of the sets of reads, without aligning them nor mapping them onto a reference genome.
Experimental evaluation In this section we test the performance of our method using simulated ("Experiments on real data" subsection) and real ("Experiments on synthetic data" subsection) datasets. In the first case, the starting point is the ground truth, that is a real. vcf file, while the synthetic data is consequently generated, starting from a real sequence, using such file and a sequencing simulator. In the second case, the starting point is real raw reads data for which the real ground truth is not available, and hence, in order to validate our results, we have generated a synthetic one by means of a standard pipeline. Note that, since the use of a synthetic ground truth can generate errors, our approach is also able to provide a further estimate of the accuracy of the identified SNPs, on the basis of the number of reads needed to identify them, as detailed in the following.
We compare ebwt2snp with DiscoSnp++, that is an improvement of the DiscoSnp algorithm: while Dis-coSnp only detects (both heterozygous and homozygous) isolated SNPs from any number of read datasets without a reference genome, DiscoSnp++ detects and ranks all kinds of SNPs as well as small indels. As shown in  [26] , DiscoSnp++ performs better than state-of-theart methods in terms of both computational resources and quality of the results. DiscoSnp++ is a pipeline of several independent tools. As a preprocessing step, the dBG of the input datasets is built, and presumed erroneous k-mers are removed. Then, DiscoSnp++ detects bubbles generated by the presence of SNPs (isolated or not) and indels, and 6. vcf stands for Variant Call Format: the standard text format for storing genome sequence variations with meta-information about position in the reference genome.
7 http://bio-bwa. sourceforge. net/. it outputs a fasta file containing the variant sequences (KisSNP2 module). A final step (kissreads2) maps back the reads from all input reads sets on the variant sequences, mainly in order to determine the read coverage per allele and per reads set of each variant. This module also computes a rank per variant, indicating whether it exhibits discriminant allele frequencies in the datasets. The last module generates a. vcf of the predicted variants. If no reference genome is provided, this step is simply a change of format from fasta to. vcf (VCFcreator module). Our framework has been implemented in C++ and is available at https ://githu b. com/nicol aprez za/ebwt2 snp. All tests were done on a DELL PowerEdge R630 machine, used in non exclusive mode. Our platform is a 24-core machine with Intel(R) Xeon(R) CPU E5-2620 v3 at 2.
40 GHz, with 128 GB of shared memory. The system is Ubuntu 14. 04. 2 LTS. Notice that a like-forlike comparison of the time consumption between our implementation and DiscoSnp++ is not possible, since DiscoSnp++ is multi-thread and our tool is currently designed to use one core only. For instance, on the real dataset, DiscoSnp++ (in the case where b = 1 ) needs about 17-18 hours for computing the SNPs when only one core is used (where the percentage of CPU usage got equal to 99%) rather than 2 h with multi-threading enabled (where the percentage of CPU usage got equal to 1, 733%). DiscoSnp++ needs, for the construction of the de Bruijn graph in the preprocessing phase, about 32 min with multi-threading enabled (where the percentage of CPU usage got equal to 274%) rather than about 1 h and 19 min when only one core is used (where the percentage of CPU got equal to 99%).
Experiments on synthetic data We propose a first experiment simulating two human chromosomes haploid reads sets obtained mutating (with real. vcf files) real reference chromosomes  8. The Table  1  Pre-processing comparative results of ebwt2snp (i. e. building the eBWT using either eGSA or BCR) and DiscoSnp++ (i. e. building the de Bruijn graph) Wall clock is the elapsed time from start to completion of the instance, while RAM is the peak Resident Set Size (RSS). Both values were taken with /usr/bin/time command. Note that for the last collection we have used a variant of BCR that keeps the ebwt(S) in internal memory. eGSA and gsacak have not been tested on the last dataset since they required too much disk space and RAM, respectively a We recall that DiscoSnp++ makes use of multiple cores while ebwt2snp is currently designed to use one core only, thus explaining the difference in speed.
Dataset Coverage Note that precision and sensitivity of DiscoSnp++ are consistent with those reported in  [26]. In their paper (Table  2 ), the authors report a sensitivity of 79. 31% and a precision of 72. 11% for DiscoSnp++ eval- uated on a Human chromosome with simulated reads (i. e. using an experimental setting similar to ours). In our experiments, using parameter b = 1 , DiscoSnp++ 's sensitivity and precision are, on average between the two datasets, 80. 77% and 73. 1% , respectively. Therefore, such results almost perfectly match those obtained by the authors of  [26]. The same Table  2  of  [26]  shows that DiscoSnp++ can considerably increase precision at the expense of sensitivity by filtering low-ranking calls. By requiring rank > 0. 2 , the authors show that their tool achieves a sensitivity of 65.
17% and a precision of 98. 73%. While we have not performed this kind of fil- tering in our experiments, we note that also in this case ebwt2snp's sensitivity would be higher than that of DiscoSnp++. Precision of the two tools, on the other hand, would be comparable. Finally, we note that also DiscoSnp++ has been evaluated by the authors of  [26]  using the SimSeq simulator (in addition to other simulators which, however, yield similar results). We remark that SimSeq simulates position-dependent sequencing errors, while our theoretical assumptions are more strict and require position-independent errors. Similarly, we assume a uniform random genome, while in our experiments we used real human chromosomes. Since in both cases our theoretical assumptions are more stringent than those holding on the datasets, the high accuracy we obtain is a strong evidence that our theoretical analysis is robust to changes towards less-restrictive assumptions.
Experiments on real data In order to evaluate the performance of our pipeline on real data, we reconstructed the SNPs between the chromosome 1 of the two 1000 genomes project's individuals HG00419 and NA19017 using as starting point the highcoverage reads sets available at ftp://ftp. 1000g enome s. ebi. ac. uk/vol1/ftp/phase 3/data/. The two datasets consist of 44,702,373 and 48,955,610 single-end reads, respectively, of maximum length 250 bases. This corresponds to a coverage of 43× and 47× for the two individuals, respectively. The input dataset of our pipeline, which includes the union of these reads and their reverse-complements, summing up to 43 Gb. Since, in this case, the real ground truth SNP set is not known, we compare the outputs of our tool and DiscoSnp++ against those of a standard SNP-calling pipeline based on the aligner bwa-mem and the postprocessing tools samtools, bcftools, and vcftools.
vcf generated at the previous step. This final validation is carried out using our own module snp_vs_vcf. The exact commands used to carry out all validation steps can be found in the script pipeline. sh available in our software repository. Note that the accuracy of the aligner/SNP-caller pipeline greatly affects the computed ground truth, which is actually a synthetic ground truth, and (unlike in the simulated datasets) will necessarily contain errors with respect to the real (unknown) ground truth. Note also that ebwt2snp outputs the coverage of each SNP (i. e. how many reads were used to call the SNP). This information can also be used to estimate the output's precision (i. e. the higher the coverage is, the more likely it is that the SNP is a true positive).
It has been shown that the highest peak energy is 7 dB lower in the spectrum of the asynchronous version. This difference is mainly due to the absence of the clock. Moreover, as the signal transitions do not occur at the same time, the spectrum tends to be spread out. At the University of Manchester, UK, S. B. Furber's team developed the AMULET2e, an asynchronous embedded microprocessor  [14]. Under the same environmental conditions, the AMULET2e EMC outperforms the synchronous ARM60 PIE microcontroller. It has been shown that because of the average periodicity of the software, the AMULET2e still presents periodic harmonics, but at a lower level than in the spectrum of the ARM60 PIE. The Quasi-Delay Insensitive (QDI) logic has also been evaluated. In  [15] , an asynchronous version of a Data Encryption Standard (DES) crypto-processor has been compared with its synchronous version.
The first step of the method constructs the Control Data Flow Graph (CDFG) of the circuit in order to analyze its architecture. This allows identification of the concurrent operations that contribute to the high current peak generation. Then, the operation latencies are estimated and the current of each operator is accordingly modeled with the used communication protocol. The last step consists of spreading the current consumption. To do that, the execution of the operations, which are not in the critical path, are locally shifted in time. To find the best location in time for each operation, while minimizing the highest current peak, the Force Directed Scheduling (FDS) algorithm is used. This method has been evaluated with a Finite Impulse Response filter. After applying the FDS algorithm, a 9 dB reduction of the uppermost current peak has been shown in this example.
Co-Simulation between the Genetic Algorithm and the Timed Petri Net Model The grey box of the Figure  2  shows the co-simulation step of the flow. The circuit Timed Petri Net is created and written in SystemVerilog, while the current evaluation and the genetic algorithm are implemented with Matlab. In order to find appropriate values for the micropipeline delays, an exchange of information between the Matlab software and the Timed Petri Net is required. A feedback loop is then created between the Timed Petri Net, the Matlab current simulation, and the genetic algorithm to find a solution. Thanks to the current simulation, and by processing the current FFT (to get an image of current spectrum), the GA provides the Timed Petri Net with a new set of delays for the circuit. This is sent using an sdf (standard delay file) file containing all the timing information for the SystemVerilog model.