dataesr/scientific-paragraphs-categorization huggingface dataset:
Also shown are MADGRAPH and POWHEG results interfaced to PYTHIA8 using CUETP8S1-HERAPDF1. 5LO and CUETP8M1. The MADGRAPH generator simulates Drell-Yan events with up to four partons, using the CTEQ6L1 PDF. The matching of ME partons and PS is performed at a scale of 20 GeV. The POWHEG events are obtained using NLO inclusive Drell-Yan production, including up to one additional parton. The POWHEG events are interfaced to PYTHIA8 using CUETP8M1 and CUETP8S1-HERAPDF1. 5LO. The predictions based on CUETP8M1 do not fit the Z boson data unless they are interfaced to a higher-order ME generator. In PYTHIA8 only the Born term (qq → Z), corrected for single-parton emission, is generated. This ME configuration agrees well with the observables in the away region in data, when the Z boson recoils against one or more jets.
In Fig. 20  and 21 the predictions at √ s = 13 TeV obtained using the new tunes from 7 TeV are shown for the charged-particle and the p sum T densities in the TransMIN, TransMAX, and TransDIF regions, defined as a function of p max T. Also shown is the ratio of 13 TeV to 7 TeV results for the five tunes. The TransMIN region increases much more rapidly with energy than the TransDIF region. For example, when using CUETP8M1, the charged-particle and the p sum T densities in the TransMIN region for 5. 0 <p max T < 6. 0 GeV is predicted to increase by 28% and 37%, respectively, while the TransDIF region is predicted to increase by a factor of two less, i. e. by 13% and 18% respectively. In Fig. 22 , predictions obtained with PYTHIA8 using CUETP8S1-CTEQ6L1 and CUETP8M1, and Tune 4C are compared to the recent CMS data measured at √ s = 13 TeV  [53]  on chargedparticle multiplicity as a function of pseudorapidity.
Predictions from CUETP8S1-CTEQ6L1 and CUETP8M1 are shown with the error bands corresponding to the uncertainties obtained from the eigentunes. These two new CMS tunes, although obtained from fits to UE data at 7 TeV, agree well with the MB measurements over the whole pseudorapidity range, while predictions from PYTHIA8 Tune 4C overestimate the data by about 10%. This confirms that the collision-energy dependence of the CMS UE tunes parameters can be trusted for predictions of MB observables. √ s = 7 TeV  [26]. The data are compared to PYTHIA8 using CUETP8M1, to MADGRAPH (MG) interfaced to PYTHIA8 using CUETP8S1-CTEQ6L1 and CUETP8M1, and to POWHEG (PH) interfaced to PYTHIA8 using CUETP8S1-HERAPDF1. 5LO and CUETP8M1. The green bands in the ratios represent the total experimental uncertainty.
Only certain Topo IV binding sites correspond to Topo IV cleavage sites To measure Topo IV cleavage at the binding sites, we took advantage of the fact that norfloxacin covalently links Topoisomerase II to the gate segment of DNA and prevent its relegation fourth and fifth rows correspond respectively to the forward and reverse raw read numbers of the parC-flag experiment. The position and orientation of genes are illustrated at the bottom of each panel. B) Sliding averages of the IP (blue, left Y axis), Input (red, left Y axis) and IP/input (green, right Y axis) data for the parC-flag experiment over 60 kb regions along the genome. To facilitate the reading, oriC is positioned at 0 and 4. 639 Mb. C) Analysis of Topo IV binding during the bacterial cell cycle. Marker frequency analysis was used to demonstrate the synchrony of the population at each time point.
Finally, we did not identify any robust consensus between sets of Topo IV cleavage sites. The only sequence traits that we identified are a bias for GC dinucleotides near the center of the sites (S7D  We explored correlations between the localization of Topo IV cleavages and binding sites of various NAPs thanks to the Nust database and tools  [32]. A significant correlation was only observed for Fis binding sites (Fig  3B ). Sixty eight genes present both Fis binding  [33]  and Topo IV cleavage (P value 2x10 -03 ). Thirty-three of the 172 manually validated cleavage sites We performed Southern blot analysis of Topo IV cleavage on representative sites to test whether gene expression and chromatin factors influenced Topo IV site selection. First, we observed that the exact deletion of cleavage sites at position 1.
Analysis of sequencing results Sequencing results were processed by the IMAGIF facility. Base calls were performed using CASAVA version 1. 8. 2. ChIP-seq and NorflIP reads were aligned to the E. coli NC_000913 genome using BWA 0. 6. 2. A custom made pipeline for the analysis of sequencing data was developed with Matlab (available on request). Briefly, the number of reads for the input and IP data was smoothed over a 200bp window. Forward and reverse signals were added, reads were normalized to the total number of reads in each experiment, strong non-specific signals observed in unrelated experiments were removed, data were exported to the UCSC genome browser for visualization and comparisons. The strongest peaks observed with NorflIP experiments (dif and 1. 9 Mb) present a characteristic shape (S6 Fig) that allows the automatic detection of lower amplitude peaks but preserves the characteristic shape.
We measured Pearson correlation coefficient with the dif and the 1. 9 Mb site for 600bp sliding windows over the entire genome. Peaks with a Pearson correlation above 0. 72 were considered as putative Topo IV cleavage sites. Sequencing data are available on the GEO Repository (http://www. ncbi. nlm. nih. gov/geo/)with the accession number GSE75641. Data were plotted with the Circos tool  [43]  and UCSC Archaeal Genome Browser  [44].
Targeting of Topo IV cleavage activity is influenced by local environmentThe bias in the distribution of cleavage sites (Fig 2H) was very similar to the Topo IV binding bias revealed by ChIP-seq (Fig 1C). NorflIP and ChIP-seq data were compared on Fig 3A. Despite the lack of corresponding ChIP-seq enrichment at the position of most highly enriched NorflIP sites, a number of consistencies were observed between these two data sets. Overall the NorflIP and ChIP-seq datasets had a Pearson correlation of 0. 3 and the averaged data (1 kb bin) revealed a Pearson correlation of 0. 5. First a small amount of local enrichment in the ChIP-seq experiments was frequently observed in the regions containing many cleavage sites (Fig 3A and 3C). This led us to consider that trapped Topo IV engaged in the cleavage reaction could contribute to a small amount of local enrichment in the ChIP-seq experiments.
Acknowledgments We thank  D. Sherratt ,  K. Marians ,  D. Grainger ,  J. Berger ,  P. Rousseau  and  F. X. Barre  for the generous gift of proteins and plasmids. We thank  Marie Franquin ,  Jorgelindo Da Veiga Moreira  and  Estelle Mignot  for preliminary experiments. We thank  Stephane Marcand  for Southern blot tips. We thank  Charlotte Cockram  for careful reading of the manuscript. We thank  Ivan Junier  and  Thibault Lepage  for technical help with Circos. We thank the  IMAGIF genomic facility  for deep sequencing.
We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the Brain-to-text system.
Microarrays Microarrays provide high resolution information of very small brain areas with a size of few square milimeters. The spatial and temporal resolution down to single action potentials is unparalleled. Microarrays in the speech-motor cortex have successfully been used to decode intended phone production  (Brumberg et al. , 2011)  for a number of isolated phones or to synthesize vowels  (Guenther et al. , 2009; Brumberg et al. , 2010). As microarrays cover only very small areas of the cortex, they might miss crucial information from other parts of the brain involved in the speech production process and might thus not be well suited in the combination with ASR technology.
, 2004). All patients gave informed consent to participate in the study, which was approved by the Institutional Review Board of Albany Medical College and the Human Research Protections Office of the US Army Medical Research and Materiel Command. Once the data was recorded, we used ASR software  (Telaar et al. , 2014)  to mark the beginning and ending of every spoken phone. See Figure  1  for a visualization of the experiment setup. To extract meaningful information from the ECoG data, we calculated logarithmic broadband gamma power between 70 and 170 Hz. Gamma power has been shown to contain highly localized task specific information  (Miller et al. , 2007; Leuthardt et al. , 2011; Pei et al. , 2011; Potes et al. , 2012). As ECoG data and acoustic data are recorded simultaneously, we can use the timings of the phones in the neural data, as well.
KEY CONCEPT 4 | ECoG Phone Models ECoG phone models can be used to estimate the likelihood that an internal of ECoG activity is a certain phone. This generative models might for example return that newly recorded data have a probability of 0. 6 of being a /l/, but only a probability of 0. 1 of being a /b/. These models for each phone can be used to estimate the likelihood of a certain phone given a piece of ECoG FIGURE 1 | ECoG and audio data are recorded at the same time. Speech decoding software is then used to determine timing of vowels and consonants in acoustic data. ECoG models are then trained for each phone individually by calculating the mean and covariance of all segments associated with that particular phone. data. Additionally, the calculated generative models for each phone can be used to gain insights into the neural basis of speech production for different phones.
Even though these ECoG phone models alone could be used to pick the most likely phone for each interval of ECoG activity, ASR software works by adding crucial information through a statistical language model  (Jelinek, 1997; Stolcke, 2002)  and a pronunciation dictionary. The combination of these three ingredients yields the great results known from speech interfaces. The ASR software extracts the search result by identifying the sequence of words from the dictionary that has the best score combination from language model and the ECoG phone models. Using these ideas from ASR, our Brain-to-text system is able to create a textual representation of spoken words from neural data. See Figure  2  for a graphical explanation of the decoding process.
CONCLUSION AND DISCUSSION In this focused review, we argue why only few brain imaging techniques can be used for ASR to produce textual FIGURE 2 | Decoding process in the Brain-to-text system. Broadband gamma power is extracted for a phrase of ECoG data. The most likely word sequence is then decoded by combining the knowledge of ECoG phone models, dictionary and language model. representations from imagined words. While no reconstruction of continuously imagined speech to a textual representation has been shown yet, we argue that measurement techniques based on electrophysiological signals are generally better suited than those based on metabolic processes. We show that ECoG is the most promising technique and demonstrate how audibly spoken speech can be recognized from ECoG data using ASR technology in our Brain-to-text system.
ICTs (Information and Communication Technologies) are responsible around 2% of worldwide greenhouse gas emissions  (Gartner, 2007). And according to the Intergovernmental Panel on Climate Change (IPPC) recent reports, CO2 emissions due to ICTs are increasing widely. For this reason, many works tried to propose various tools to estimate the energy consumption due to software in order to reduce carbon footprint. Since a few years, we have been able to find several research, on the web tools,  (Power Supply Calculator, 2014) ,  (eXtreme Power Supply Calculator, 2006) , (Computer Power Consumption Calculator) that allow estimating the energy consumed by each component of a computer. Doing so, the user chooses the feature of the component and an estimation is given about related power consumption.
Devices used We used the laptop ASUS model N751J composed of a CPU Intel Core i7-4710HQ (2. 5GHz) and a RAM 16 Go (2 * 8 Go) DDR3 1600 MHz. To run tests, we developed a tool TEEC (Tool to Estimate Energy Consumption), whose model is shown in Figure  2 , in Java programing language because depending on  (Noureddine, 2012)  Java represent the language with the least power consumption during compilation and execution steps in default parameter settings of the compiler. In this tool TEEC, we use Sigar library  (Morgan and MacEachern, 2010 ) in order to get information about the CPU and the RAM. Moreover, we use also the parameter provides by manufacturers. And, Java Agents allows us to the instrumentation capabilities to an application. Thus, using TEEC, we realized several different tests in order to observe the variation of the power consumption due to the CPU and the memory and compare them.
CONCLUSIONS A modelization of the CPU and the DRAM has been made in order to understand the behavior and the functionality of each component. Thanks to this model, several mathematical formulas have been established to estimate the power consumption due to each part of each component. Thus, based on this methodology, a tool that allow to measure the power consumed by CPU and DRAM has been implemented and named TEEC (Tool to Estimate Energy Consumption). This tool gives accurate and efficient information about CPU and DRAM power consumption, has been used to perform some experiments. The goal of these tests was to observe the impact of the code source of an application in the power consumption. These experiments have provided several results. When the code source is optimized, it is possible to reduce the power consumption due to CPU.
Introduction The goal of this paper is to develop linear algebra for elementary divisor rings, that is, rings where there is an algorithm for computing the Smith normal form of matrices. More specifically, we focus on the axiomatics and basic algorithms of such rings. This work fits within a bigger project of formalizing theories and algorithms for constructive module theory. This lays ground to program more efficient algorithms (like in Axiom, Maple, Magma,. ), and prove them correct with regard to the notions introduced here, using the CoqEAL methodology  [12, 5] , developed by some of the authors (cf. section 7). In this paper, we do not contribute with new theorems or new efficient algorithms in the field, except for some minor factoring in order to simplify the formal proof. However, we provide a formal framework to develop more theory and more complex algorithms in Coq, by regrouping and representing folklore concepts in the proof assistant, that we found scattered in the literature.
Classical axiomatics and constructive variants The algorithms we present for computing the Smith normal form can be seen as generalizations of Gaussian elimination that can, in particular, be defined for Z. The main source of inspiration for this work is the formalization of finite dimensional vector spaces by Georges Gonthier  [17]  in which spaces are represented using matrices and all subspace constructions can be elegantly defined from Gaussian elimination. This enables a concrete and point-free presentation of linear algebra which is suitable for formalization as it takes advantage of the small scale reflection methodology of the SSReflect extension and the Mathematical Components library  [18]  for the Coq proof assistant  [8]. When generalizing this to elementary divisor rings there are two essential problems that need to be resolved before the theory may be formalized:  (1)  What is a suitable generalization of finite dimensional vector spaces when considering more general classes of rings than fields as coefficients? (2) What rings are elementary divisor rings? A possible answer to the first problem is finitely generated R-modules, i.
A morphism ϕ between finitely presented modules M and N given by presentations: R m 1 R m 0 M 0 R n 1 R n 0 N 0 M N is represented by a m 0 × n 0 matrix ϕ G and a m 1 × n 1 matrix ϕ R such that the following diagram commutes: R m 1 R m 0 M 0 R n 1 R n 0 N 0 M ϕ R ϕ G ϕ N The intuition why two matrices are needed is that the morphism affects both the generators and relations of the modules, hence the names ϕ G and ϕ R. In this paper we adopt the SSReflect convention that composition is read in diagrammatic order (i. e. from left to right) when writing equations obtained from commutative diagrams. This means that the equation related to the above diagram is written M ϕ G = ϕ R N. In order for us to be able to compute kernels of morphisms we need to assume that the underlying ring is coherent so that we can solve systems of equations involving the underlying matrices.
Acknowledgments The authors would like to thank  Thierry Coquand  and  Henri Lombardi  for interesting discussions. The authors are also grateful to  Dan Rosén  and  Jean-Christophe Filliâtre  for 28 G. CANO,  C. COHEN ,  M. D ÉN ÈS ,  A. M ÖRTBERG, AND V. SILES helping us explore the Kaplansky condition using various automatic theorem provers. We would also like to thank  Claire Tête  for useful comments on a preliminary version of the paper. Finally we would like to thank the anonymous reviewers for their helpful remarks.
The first voice manipulation, creating low-pass filtered vocalizations ('alpha ratio'), was based on the above-mentioned observation that increased spectral energy in high-frequency bands above 1 kHz or the relative higher energy in high-compared to low frequency bands is an important acoustic feature of joyful and aggressive voices, which makes them distinct from other affective vocalizations, such as fearful or sad vocalizations  (Juslin and Laukka, 2003; Leitman et al. , 2010a; Patel et al. , 2011). For this manipulation, we used a low-pass filtering approach in the Praat software  (Boersma, 2001). Low-pass filtering was done by using a Hann pass band filter of 0-1 kHz, including a roll-off of 100 Hz. This filtering nearly eliminated all energy of frequencies above 1 kHz, and thus led to a significant drop of the alpha ratio for these modified vocalizations.
The second manipulation ('f0 SD ') was based on the observation that increased pitch variations and pitch dynamics is a central feature of many affective vocalizations, especially of joyful and aggressive vocalizations  (Banse and Scherer, 1996; Juslin and Laukka, 2003; Leitman et al. , 2010a). For the second manipulation of vocalizations with reduced f0 variation, we determined the f0 standard deviation for each joyful and aggressive voice, as well as in neutral expressions spoken by the same actors. We used the STRAIGHT toolbox for Matlab (www. wakayama-u. ac. jp/$kawahara/STRAIGHTadv/index_e. html) to determine the f0 in each vocalization, as well as for the resynthesis of the vocalizations with a reduced f0 standard deviation. We first extracted the f0 standard deviation of all neutral vocalizations of the same words by the same actor.
We have to note here that native and manipulated neutral voices could not be included in the experimental design as additional conditions, because the f0 variation of neutral voices served as reference for the manipulation of aggressive and joyful voices. Finally, the third manipulation of formant-filtered vocalizations ('form') was based on the observation that many higharousing vocalizations usually show a reduced HNR and thus more 'noisiness' or 'roughness' because of an increased spectral energy between the harmonic frequency bands, especially of vocal expressions of aggression or fear  (Patel et al. , 2011). To increase the HNR of affective vocalizations, we therefore used formant filtering as implemented in the Praat software to increase the 'harmonicity' of the vocal stimuli. Vocal harmonicity is represented by voice formants that result when sound waves with a certain f0 are emitted from vocal fold vibrations and passed through the vocal tract.
5 kHz, and all acoustic stimuli were scaled to a mean sound pressure level (SPL) of 70 dB. This scaling was done with the Praat software. We scored the main acoustic features of the final stimuli to ensure that the manipulation resulted in the elimination of the intended feature while other features were unaffected. These scores were subjected to a 2 Â 4 repeated measures analysis of variance (ANOVA) with the within-subject factors affective valence (aggressive, joyful) and manipulation condition (nat, alpha, f0 SD , form). The low-pass filtering of the voices resulted in a significant decrease of the alpha level for these stimuli indicated by a significant main effect of the factor manipulation condition (F 3,93 ¼ 112. 003, P ¼ 9. 17 Â 10 À31 ). Planned post-hoc comparisons revealed a significant difference of the alpha level of low-pass filtered voices compared with the alpha level of native voices, as indicated by a Bonferroni-corrected post-hoc comparison (P ¼ 1.
A blank screen appeared after the offset of the auditory stimulus. After the presentation of the voices, participants had to indicate the affective valence of the vocal expressions in a two-alternative forced-choice decision task (aggressive or joyful). Participants responded with their right index and middle finger, and response buttons were counterbalanced across participants. We presented a total of 304 trials, including 48 silent events with no auditory stimulation. To localize human voice-sensitive regions in the bilateral STC, we used 8 s sound clips taken from an existing database  (Belin et al. , 2000). These sound clips contained 20 sequences of human voices and 20 sequences of animal or environmental sounds. Each sound clip was presented once with a fixation cross on the screen and a 4 s gap between each clip.
These boxcar functions were convolved with a canonical hemodynamic response function. Separate regressors were created for each experimental condition. Six motion correction parameters were finally included as regressors of no interest to minimize false positive activations that were due to task-correlated motion. Linear contrasts for the experimental conditions for each participant were taken to a second-level random effects analysis. For the functional data of the main experiment, we performed a searchlight decoding analysis as implemented in the PyMVPA package (http://www. pymvpa. org/) for the purpose of an information-based brain mapping according to multivoxel activity patterns  (Kriegeskorte et al. , 2006; Kriegeskorte and Bandettini, 2007) , which differ between the experimental conditions.
Behavioral data Participants had to indicate if they heard an aggressive or a joyful voice while listening to native and to feature-manipulated voices, and while their brain response were recorded using high-field magnetic resonance imaging. Reaction times (RTs) and percentage error rates of their classifications according to the affective valence were subjected to a 2 Â 4 repeated measures analysis of variance (ANOVA) with the within-subject factors affective valence (aggressive, joyful) and manipulation condition (nat, alpha, f0 SD , form; Figure  1B ). RTs did not differ across the affective valence (F 1,12 ¼ 3. 674, P ¼ 8. 75 Â 10 À2 ), but did differ across the different types of manipulation conditions (F 3,36 ¼ 7. 376, P ¼ 9. 21 Â 10 À4 ). There was also an valence Â manipulation interaction (F 3,36 ¼ 8.