dataesr/scientific-paragraphs-categorization huggingface dataset:
The SAMpler for PICosecond time (SAMPIC) chip has been designed by a collaboration including CEA/IRFU/SEDI, Saclay and CNRS/LAL/SERDI, Orsay. It benefits from both the quick response of a time to digital converter (TDC) and the versatility of a waveform digitizer to perform accurate timing measurements. Thanks to the sampled signals, smart algorithms making best use of the pulse shape can be used to improve time resolution. A software framework has been developed to analyse the SAMPIC output data and extract timing information by using either a constant fraction discriminator or a fast cross-correlation algorithm. SAMPIC timing capabilities together with the software framework have been tested using pulses generated by a signal generator or by a silicon detector illuminated by a pulsed infrared laser.
The analysis is performed with a dedicated software framework developed to analyse SAMPIC data. In Section 2, we give a brief description of the SAMPIC chip, stressing the advantages with respect to previous technologies. Section 3 is dedicated to the hardware and the online software used to acquire data. The offline software employed for timing reconstruction is presented in Section 4 while the tests of the SAMPIC chip are presented in Section 5, first with synthetic signals and then using fast silicon detectors. Finally, a summary of the SAMPIC performance measured in this paper is reported in Section 6.
Performance measured Comment Technology AMS CMOS 0. the acquisition, displaying and saving data is also available. The software also allows to perform real-time measurements on the signals. The full device, shown in Figure  2 , requires a simple 5 V power supply and a PC that can be connected through USB 2. 0, Gigabit Ethernet or optical fibre. Moreover, it is possible to supply an external clock and some control signals, for instance to setup an external trigger or a signal that vetoes the trigger. The device has 16 or 32 MCX connectors for the analog input, depending on the number of SAMPIC chips used (1 or 2). Trigger parameters: trigger mode, thresholds and polarity of the signal. Acquisition parameters: data format (ASCII or binary) and maximum file size, length of the acquisition (time or number of hits).
Moreover, it is possible to correct the acquired data through the control software  [19]. Two types of calibration are implemented: the first one improves the ADC linearity of each cell and the second one corrects the non uniformity of the sampling period in the analog memory, which is related to the chip architecture and geometry. The calibration procedure needs to be repeated for each set of acquisition parameters (i. e. sampling frequency and number of ADC bits). The software takes care of storing the configuration parameters for all the different acquisition scenarios and makes use of them to correct the acquired data. It should be noted that without time calibrations, the SAMPIC chip is still able to achieve a very precise time resolution, of the order of 15 ps RMS at 6. 4 GS/s  [19].
Data format definition The data acquired using the SAMPIC chip are processed by the acquisition board and can be saved in a binary or ASCII file using the control software. A preliminary estimation of the time differences can be made online using the latter. However, an offline analysis is very useful during the development stage in order to compare easily several time algorithms and implement some data cleaning and/or pre-processing if required. The offline software is a C++ code based on the ROOT framework. 5 First, the sampled waveforms are extracted from the data acquired with the online software and stored in a hierarchical structure (TTree). Data are organized according to their timestamps. In particular, if an external trigger is provided, two or more hits are considered belonging to the same "event" when the differences between the channel and the trigger timestamps are smaller than ∆t max , after being corrected for a fixed trigger latency.
Otherwise, all coincident hits, i. e. with a time difference between their respective starting time less than ∆t max , are assigned to the same event. The value of ∆t max needs to be set by the user. For each acquisition, the baseline, i. e. the average sampled voltage when no event occurs, is measured from the first n b recorded points, where n b must be specified by the user. It is then recorded in the data tree. The first n b recorded points are also used to compute for each channel the RMS noise, which is defined as the RMS of the average sampled voltage when no event occurs. Other relevant information such as sampling period, channel number and ADC counts are also saved. This intermediate step is useful to disentangle the data acquisition from the analysis part, foreseeing the usage of the same analysis code for future version of the hardware and with other sampling devices such as an oscilloscope.
Time measurement analysis Several algorithms have been proposed to extract the timing of a digitized signal  [21, 22, 23, 24, 25, 26, 27, 28, 29]. Two time reconstruction algorithms have been selected among them and implemented in the offline software because of their known performance, their simplicity and their limited resources requirement for a potential hardware implementation. They can be run on any SAMPIC data tree via the offline software: • Constant Fraction Discriminator (CFD)  [25, 26, 27]. • Cross-Correlation (CC)  [27, 28, 29]. For precise timing, the most common approach to compute the time of arrival of a sampled signal is to use a CFD algorithm  [25, 26, 27]. The arrival time is then defined as the instant when the signal crosses a threshold corresponding to a given fraction 0 < R < 1 of the signal amplitude, set by the user beforehand.
The computation time for all CFD-based algorithms implemented in the offline software is of the order of 1 ms per event. 7  The alternative method implements a cross correlation algorithm  [27, 28, 29]. A correlation function is computed between the acquired signal and a template extracted from an independent set of acquisition with high statistics. The latter is usually built from a reduced time window around the rising edge of the signals. The maximum of the correlation function, obtained by varying the delay between the signal and the template, indicates the optimal superposition of the two. Let s be a signal sampled by SAMPIC and n the number of points of the corresponding template t, defined in a sliding time sub-range of the SAMPIC acquisition window [delay, delay +n-1]. We recall that s is defined in the whole SAMPIC acquisition window, which depends on the sampling frequency (see Section 2).
However, the CC requires the generation of templates for the expected signals, which might not be straightforward for certain application with relatively high statistical fluctuations of the signal like particle detection with thin detectors. It should be noted also that the computation time of the CC algorithm is significantly higher than for the CFD algorithm by about a factor 100. In conclusion, the offline software is able to measure the time difference between two signals via various signal algorithms (CFD, refined CFD, CC). The signal-template synchronizations via cross correlation are also recorded in the output. In Section 5, results from experimental tests are reported. In those tests, the time difference between the signals is fixed and the measurement with SAMPIC is repeated many times under similar conditions.
Experimental measurements Different tests were undertaken to measure the SAMPIC performance and compare the results of the various algorithms implemented in the offline software. First, some preliminary tests done using signal synthesized by a signal generator are reported in Section 5. 1. Then, in Section 5. 2, the SAMPIC chip and offline software are tested together with ultra-fast silicon detectors (UFSDs)  [32, 33]  illuminated by a pulsed infrared laser. The UFSDs are new generation silicon detectors being developed at INFN, Torino, UC Santa Cruz and the Institute of Microelectronics of Barcelona for fast timing application. More details will be given in Section 5. 2.
Tests with ultra-fast silicon detectors In order to test the SAMPIC chip and the offline software with signals generated by real particle detectors, a particular design of Low Gain Avalanche Diodes-based detectors (LGAD) are used  [34]  called ultra-fast silicon detectors (UFSDs)  [32, 33]. Low Gain Avalanche Diodes (LGAD) are a novel type of silicon detectors that combine the advantages of internal gain, as in avalanche photo-diodes (APDs), with the properties of standard silicon detectors. The key idea is that the low gain will not generate additional dark counts and excessive leakage current as it happens in APD, but will offer an enhanced signal that can be used for timing applications. Starting from this idea, Ultra-Fast Silicon detectors were proposed, optimizing the LGAD idea for timing measurements.
The laser has a bandwidth of 2 nm and includes a tunable amplitude. The jitter between the two laser pulses is smaller than 3 ps RMS. • Ultra-fast silicon detectors of gain  10 [33, 35]  polarized with a voltage of 800 V are used. • The signals are amplified with CIVIDEC C2 broadband amplifiers (BDA). BDAs read the currents generated by the detectors on the amplifier input resistance (50 Ω) and amplify them at 40 dB. The amplifier has a bandwidth of 2 GHz, or, equivalently, a rise time of 180 ps. A C6 fast charge sensitive amplifier (CSA) will be used for the second part of the tests (see Section 5. 2. 2). • The two active SAMPIC channels are configured to sample data at 6. 4 GS/s (C2-BDA) or 3. 2 GS/s (C6-CSA). • Data is acquired on a computer and processed by the offline analysis software.
Conclusion In this article, we reported the first results on the timing measurement resolutions obtained by the SAMPIC V1 chip, which is the version in use as of today. The SAMPIC chip measures the time of the input signals based on their fast sampling. In this paper, experimental results with sampling speed of 6. 4 and 3. 2 GS/s are reported. In order to analyse SAMPIC data, an online and offline software have been developed to acquire the sampled waveforms and perform accurate timing measurements. In particular, the offline software implements two Constant Fraction Discriminator (CFD) algorithms and one cross-correlation (CC) algorithm which can be compared. Performance studies of various signal processing algorithms applied on waveform data produced by other digitizers than SAMPIC have already been performed, for example in  [36].
This feature could be very useful to improve the resolution of the measurements without changing the apparatus. It also demonstrates the power of signal processing to improve timing measurement performance and encourages a further development of the offline software. Finally, the start of the resolution plateau of SAMPIC with respect to the amplitude is close to 300 mV for UFSD signals, which should therefore be the aim in the context of the design of a detection chain willing to use the SAMPIC chip. However, one should keep in mind that the experiment apparatus used for the SAMPIC tests reported in this paper differs slightly with respect to the potential applications: • For high-energy physics and medical imaging (PET) applications, the expected signal is arising respectively from charged protons and gamma photons, and not infrared photons.
The goal of the Conversational Interfaces project at CSLI is to develop a general purpose architecture which supports multi-modal dialogues with complex devices, services, and applications. We are developing generic dialogue management software which supports collaborative activities between a human and devices. Our systems use a common software base consisting of the Open Agent Architecture, Nuance speech recogniser, Gemini (SRI's parser and generator), Festival speech synthesis, and CSLI's "Architecture for Conversational Intelligence" (ACI). This paper focuses on one application of this architecture -an intelligent tutoring system for shipboard damage control. We discuss the benefits of adopting this architecture for intelligent tutoring.
AN ARCHITECTURE FOR MULTI-MODAL DIALOGUE SYSTEMS To facilitate the implementation of multi-modal, mixed-initiative interactions we use the Open Agent Architecture (OAA)  (Martin et al. , 1999). OAA is a framework for coordinating multiple asynchronous communicating processes. The core of OAA is a 'facilitator' which manages message passing between a number of encapsulated software agents that specialize in certain tasks (e. g. , speech recognition). Our system uses OAA to coordinate the following agents: • The Gemini NLP system  (Dowding et al. , 1993) : Gemini uses a single unification grammar both for parsing strings of words into logical forms (LFs) and for generating sentences from LF inputs (although, as mentioned above, we do not use this feature currently). This agent enables us to give precise and reliable meaning representations which allow us to identify dialogue moves (e.
g. , wh-query) given a linguistic input; e. g. , the question "What happened?'' has the (simplified) LF: whquery(wh  ([tense(past) ,action(happen)])). • The Nuance speech recognition server: The Nuance server converts spoken utterances to strings of words. It relies on a language model, which is compiled directly from the Gemini grammar, ensuring that every recognized utterance is assigned a LF.
Evaluation As part of the research described here, we have done only informal evaluation of the system so far. Three former damage control assistants, with no previous experience using our tutor, completed a session with our tutor. Each subject was able to complete a dialogue with our system, and data has been collected, including speech recognition error rates. All three dialogues were recorded, and the Information States logged. We are planning two larger evaluation efforts in the near future: one at Stanford University, the other at the Naval Postgraduate School in Monterey, CA. We are also planning several psycholinguistic experiments utilizing our tutor. Broadly, we would like to find out if students learn better with a spoken environment. If so, is that directly because of the naturalness of the modality, or because speech encourages longer utterances and more student initiative?.
As proof of concept, we sequenced the genome of Xoo PXO83, a race 2 strain that clusters with race 3 and race 5 strains in RFLP analysis  31  and which differs from the sequenced race 6 Xoo strain PXO99 A. PacBio sequencing of approximately 170x coverage resulted in a single contig despite the presence of numerous repetitive elements (Table  2 ). Trimming contig ends and circularization yielded a final PXO83 chromosome of 5,025,428 bp (Genbank accession no. CP012947). To assess the quality of the final PXO83 assembly, we performed a re-sequencing experiment using the same PacBio reads that were used for the assembly. We found a generally homogeneous coverage of the assembled chromosome (Supplementary Fig. S1 ), with a mean coverage of approximately 182. We investigated the stability of the results by artificially reducing the set of reads by sub-sampling (Supplementary Fig.
3a ). The suite of applications is available at "http://www. jstacs. de/index. php/AnnoTALE" as a JavaFX-based stand-alone application with graphical user interface for interactive analysis sessions. In addition, we provide a command line application that may be integrated into other pipelines. Both are based on the open-source Java library, Jstacs  34  , and use identical code for the actual analysis, ensuring consistent results between both versions. In the following section, we use the newly sequenced genome of race 2 Xoo strain PXO83 and its TALE repertoire to exemplify the steps of the AnnoTALE pipeline (Fig. 3a ).
If the average divergence score of the TALE and the most similar class does not exceed the threshold, the TALE is added to that class. Otherwise, it becomes the first member of a new class. This assignment is implemented by the "TALE Class Assignment" tool of AnnoTALE. Considering the TALEs of PXO83 identified in the work reported here, one TALE could not be assigned to any existing class and thus became the founding member of a new class, and was assigned the name TalCA1 (Supplementary Fig. S11f ). All of the other TALEs show sufficient similarity to previously identified TALEs and were assigned to one or the other of the existing classes (Table  3 ; Supplementary Sequences). While this manuscript was in preparation, two additional complete Xanthomonas ssp. genomes were published: Xoo PXO86 and Xoc CFBP7342 (Genbank accession nos.
e. , all TALEs in all classes). The list of targets can be found as Supplementary Table  S4. Applying this tool to the TALEs in all 62 classes, we find, for instance, that the four TALEs in class TalAP share 11 target genes, of which the target gene OsHEN1 of TalAP1 (PthXo8) and TalAP2 (XOO1138) 2,23 is predicted on rank 1 for all four TALEs. Interestingly, OsHEN1 is also the target gene of TalAK2 (Tal1c) from Xoc BLS256, but this TALE targets a different site within the OsHEN1 promoter using different RVDs. The current list of TALEs and their classifications can be accessed at www. xanthomonas. org. To reserve a name for a given TALE in the course of manuscript preparation, confidential requests can be sent to the corresponding authors of this paper. Following publication, these new names will be included in our updated web-based class list that is also used by AnnoTALE.
Automated annotation of TALE genes. For the automatic annotation of TALE genes, genomic DNA sequences of known TALEs from fully sequenced Xoo (PXO99 A , MAFF311018, KACC10331) and Xoc (BLS256) strains were collected and each split into the regions coding for i) N-terminus, ii) tandem repeats that are part of the binding domain, and iii) C-terminus. For each of these regions, separate hidden Markov models were learnt using HMMer 48 based on their DNA sequences. Domain occurrences were detected using custom Java code to avoid dependencies on HMMer, allowing for downstream analyses in one stand-alone application. Since arrays of tandem repeats are a unique feature of TALE genes, genomes were first scanned for contiguous arrays of  matches to the HMM for these repeats. For each array detected, upstream and downstream regions were scanned for matches to N-terminus and C-terminus, respectively, yielding an initial region for each putative TALE gene.
If in one column the gap symbol was more frequent than any of the AAs, this column was removed from the consensus. Extracted genomic TALE DNA sequences (see above) were translated to the corresponding protein sequences in silico. As reference, prototypic TALE protein sequences were built by concatenating the consensus of N-terminus, k tandem repeats, the last half repeat and the C-terminus. For each number k of tandem repeats, a pairwise alignment between the protein sequences of the given TALE and the prototypic TALE sequence was performed (BLOSUM62 substitution matrix  50  , affine gap costs: gap open penalty 3, gap extension penalty 2). That k yielding the best alignment score was chosen. The TALE sequence was then split into those parts induced by the alignment, e. g. , the DNA sequence coding for the third repeat of a TALE was extracted as those nucleotides that code for the amino acids aligned to the third repeat in the prototypic TALE sequence.
The split repeat sequences were the input of the automated extraction of RVDs in the next step. Automated extraction of RVDs. Given one repeat sequence extracted in the previous step, the corresponding protein sequence was aligned to the consensus repeat. In the consensus repeat, the RVD comprises AAs 12 and 13. Hence, those AAs in the given repeat sequence that aligned to those AAs were extracted. For this alignment, a BLOSUM62 substitution matrix and affine gap costs with gap open penalty of 3 and gap extension penalty of 1 were used, to account for the existence of repeat of aberrant lengths  35. Computing p-values of pairwise TALE alignments/distances. Conceptually, the p-value of a TALE distance (resulting from pairwise alignment) was determined as the probability of finding the same or a lower distance by comparing a TALE against a database of random TALE sequences.
Acknowledgements This work was supported by grants from the  Deutsche Forschungsgemeinschaft  ( BO 1496/8-1  to JB and  GR 4587/1-1  to JG) and a grant from the  European Regional Development Fund of the European Commission  to JB and by the  COST action FA1208  "SUSTAIN". The authors thank  Carola Kretschmer  for technical assistance,  Romina Plötner  for infection studies,  Ulla Bonas  for support, and  Stefan Posch  for valuable discussions.
To accurately measure the evolution of the radio galaxy luminosity function, particularly at the bright end, it is necessary to conduct very wide-field radio/X-ray surveys while maintaining good sensitivity. For this purpose, we present the first results from the pilot program of a large radio continuum survey with the Australia Telescope Compact Array (ATCA) to cover 25 deg 2 of the XXL survey field. The XXL survey comprises the largest XMM-Newton project approved to date  (Pierre et al. 2015, Paper I hereafter) ; it has provided 3 Ms of new data, and more than 6 Ms when including archival data. The main goals of this X-ray survey are to provide long-lasting legacy data for studies of galaxy clusters and AGNs and to constrain the dark energy equation of state using clusters of galaxies. Observations with XMM-Newton are essentially complete; a few fields are undergoing re-observation.
Data reduction We used the Multichannel Image Reconstruction, Image Analysis and Display (MIRIAD) software package to reduce the data. In the calibration step we used 16 frequency bins in the GP-CAL (gain/phase/polarisation calibration) task (i. e. bin width of 128 MHz). The 81 pointings were individually reduced and imaged. Automated flagging was performed using the MIRIAD task PGFLAG. This is based on AOFLAGGER and was developed for LOFAR data sets, but has now been applied to various radio data from other instruments  (Offringa et al. 2010 (Offringa et al. , 2012)). We found that the lowest frequency sub-band, which was centred at 1. 204 GHz, was significantly affected by radio frequency interference (RFI). In the shortest baselines 80 -90 percent of the data in this sub-band was flagged, and even in the long 6 km baselines 60 -70 percent of the data was affected by RFI.
Imaging Wideband receivers such as CABB on the ATCA present new challenges to radio imaging. The primary beam response, the synthesised beam, and the flux density of most sources vary significantly with frequency over the 2 GHz wide bandwidth. One method used to mitigate these issues is to divide (u, v) data into sub-bands and then to force similar beam sizes with an appropriate "robustness" parameter  (Briggs 1995). This approach was used to image VLA data spanning 2 -4 GHz  (Condon et al. 2012; Novak et al. 2015; Smolčić et al. 2014b ). We tested two imaging schemes, one where the (u, v) data are not divided into sub-bands and a second scheme where the 2048 MHz CABB band is divided into 256 MHz sub-bands. We find that the effect of bandwidth smearing is more significant in the first approach, thus hereafter we focus and describe in detail only the second imaging approach.