dataesr/scientific-paragraphs-categorization huggingface dataset:
Hardware implementations contain an additional bit in the mantissa field, also called hidden bit  [11]. In this work, the hidden bit is not counted in the mantissa bit-width M. Definition 2. The machine epsilon (or machine precision) of an FP format (E, M) provides an upper bound on the relative error caused by rounding. It is defined as M = 2 -M (1) The machine epsilon M also constitutes the distance from 1. 0 to the next larger FP number  [34]. For this work, only software Application Binary Interfaces (ABI) that support FP arithmetic in hardware (i. e. , hard-float ABI) are considered. We also focus on CPU architectures and precisely on the 64-bit RISC-V  [35]  Instruction Set Architecture (ISA), with single-precision (F) and double precision (D) extensions. This architecture is referred to as "RV64FD" and will constitute our reference baseline FPU architecture.
Software Implementation Aspects In the following sections, an open-source Jacobi implementation  [40]  written in C is considered. It takes as an input a randomly generated 50 × 50 diagonally dominant matrix A, with pseudo-random values between 0 and 1. The default target tolerance threshold is set to 10 -10 unless otherwise is specified. Gauss-Seidel is also implemented following the same structure of the original Jacobi application. The software applications have been cross-compiled for the Proxy Kernel execution environment (a lightweight bare-metal-like OS dedicated to RISC-V-based systems)  [41] , using the RISC-V GCC compiler in a similar fashion to the cycle-accurate study performed earlier in Section 3. 2. 1.
Positive percentage values signify an increase in the number of iterations, whereas negative ones mean that there was a reduction in the number of iterations. Furthermore, the conservative policies do not alter the number of iterations much compared to the double precision reference: they achieve a bit less than ±1. 5%. However, the relaxed policies have a slightly important but scattered effect on iterations' count. For example, for a threshold of 10 -4 (Figure  13 ), relaxed policies achieve a -17% to -1% reduction in the number of iterations for more than 75% of inputs. At 10 -12 (Figure  14 ), nearly half of the input dataset achieves a variation between -1% and +2. 5%.
Worst Case/Best Case HW-Level Evaluation With everything said in Section 7 in mind, it is important to perform an empirical HW-level evaluation. Only edge cases where VPT affects either negatively or positively the energy consumption of the algorithm are considered since it would take months to perform the HW-level study for all the 1000 inputs. Moreover, for the sake of concision, only the conservative thresholds generated with Gappa in the case of threshold policy (1) are considered. Furthermore, for threshold policy (2), only the case where SMI is set to 4 is evaluated. The edge cases have been chosen from the 1000-input dataset based on the software simulation presented in Section 7. They correspond to the input couples (A, b) that produce the lowest and the biggest iterations' overhead for each threshold in Figures  13  and 14.
Conclusions This work proposes a new method and an FPU architecture that enable designers to dynamically tune FP computations' precision automatically at run-time called Variable Precision in Time (VPT). In spite of its circuit area overhead, the proposed approach simplifies the integration of variable precision in existing software workloads at any level of the software stack (OS, RTOS, or application-level): it only requires lightweight software support and solely relies on traditional assembly instructions, without the need for a specialized compiler or custom instructions. The technique was applied to the Jacobi and the Gauss-Seidel iterative methods taking full advantage of the suggested FPU. For each algorithm two threshold policies were proposed: a conservative policy (1) and a relaxed policy (2).
Limitations To choose between the two threshold policies for a given problem, the user/designer should use representative datasets and especially evaluate the memory-related aspects, especially for threshold policy (2). Furthermore, the memory-related aspects should be studied further to evaluate whether the cost of the potential added iterations (hence more load and store operations) is lower than the gain associated with computation optimization. The proposed methodology is application-dependent, i. e. , the designer should transform the algorithm manually, ensure convergence after modification, and select adequate intermediate tolerance thresholds depending on the convergence metric used in the application.
The COSI FOV and localization capabilities make it powerful for searches of electromagnetic counterparts to gravitational wave and high-energy neutrino detections. The heart of COSI is a stacked array of germanium cross-strip detectors, which provide high efficiency, high resolution spectroscopy, and precise 3D positioning of photon interactions. As COSI is a Compton telescope, 3D positioning is required to carry out the Compton event reconstruction. We accomplish this using the versatile MEGAlib software package  [30]. The detectors are housed in a cryostat and cooled to <90 K. They are shielded on five sides, reducing the background and defining the FOV. The COSI shields are active scintillators, extending COSI's FOV for detection of GRBs and other sources of gammaray flares. COSI will be in low-Earth orbit and will spend most of the mission time in a survey mode, alternately pointing 22 • North of zenith to 22 • South of zenith every 12 hours to enable complete sky coverage over a day (see Figure  1 ).
No other technology thus far tested exceeds COSI's spectral resolution and polarization capability in this energy range. The COSI array of 16 GeDs is housed in a common vacuum cryostat cooled by a CryoTel CT mechanical cryocooler. The GeDs are read out by a custom ASIC, which is integrated into the full data acquisition system. An active BGO shield encloses the cryostat on the sides and bottom to veto events from outside the FOV. The COSI instrument is shown in Figure  4. We have developed the COSI instrument through NASA's Astrophysics Research and Analysis (APRA) program, including highaltitude balloon flights in 2005, 2009, 2014, and 2016, providing a very important proof of concept. During a 46-day flight in 2016, the COSI imaging, spectroscopy, polarization, and real-time localization capabilities were demonstrated.
We were able to detect, localize, and report GRB 160530A in real time  [25]. An analysis of the GRB 160530A data resulted in an upper limit on the polarization of <46%  [16] , and new techniques were developed for polarization studies  [17]  and for calibrating the instrument  [15, 29]. The detection of the 0. 511 MeV emission and information about the line shape and spatial extent is reported in  Kierans et al. ([12] ). We have also used calibration measurements to benchmark the detector effects engine  [23]. The data from the 2016 flight have also been vital for completing the software pipeline for the COSI imaging technique. We have demonstrated the modeling of extended source distributions, such as the 0. 511 MeV emission from the Galactic bulge  [20] , and also used the Crab nebula and pulsar as a point source for testing the technique  [31].
Data Challenge Project To improve the user-friendliness of the COSI analysis software and to prepare the high-energy community for analyzing COSI data, the COSI group is carrying out a Data Challenge project. For this project, the COSI team will publicly release a set of astrophysical source simulations, the full COSI imaging response, and documentation of our python-based analysis software (COSIpy). 1. 0 × 10 -5 Line Sensitivity  (1. 809 MeV) 3. 0 × 10 -6 Flux limit for polarization b 1. 4 × 10 -10 GRB polarization fluence limit c 5. 0 × 10 -6 a 3 narrow line sensitivity in 2-years of survey observations in units of photons cm -2 s -1. b Detection limit for 2-years of survey observations in units of erg cm -2 s -1. c For 50% minimum detectable polarization. The fluence limit is in units of erg cm -2.
Conclusions and Schedule COSI will open up the MeV energy band to explore the science of positrons, nucleosynthesis, polarization studies of GRBs and accreting black holes, and multimessenger astrophysics. With its large FOV and observing strategy, it will cover the entire sky in the 0. 2-5 MeV bandpass every day. The COSI team will be starting the Data Challenge in the latter part of 2021, and updates will be provided on the COSI website (http://cosi. ssl. berkeley. edu). If COSI is selected for flight, it will enter Phase B in January 2022 and launch in late-2025.
SAMPLE AND DATA We search the literature for CO observations of local and high-redshift galaxies, and seek for galaxies which have multiple CO line detections. This is not a complete search, but we have included 132 papers presenting CO observations from 1975 to 2020 2. We require a galaxy to have one low-J CO line, CO(2-1), and one mid/high-J CO line, , for this work. This approach is chosen to maximize the sample size while covering most high-redshift main-sequence (MS) 3  galaxies' CO observations. We also require multi-wavelength coverage including optical, near-IR, far-infrared and (sub-)millimeter, in order to fit their panchromatic SEDs and obtain stellar and dust properties. In this way, we build up a sample of 76 galaxies. They are divided into the following subsamples: • 22 "local (U)LIRG": local (ultra-)luminous infrared galaxies with IR luminosity L IR ≥ 10 11 L.
We compared our CO(2-1) line fluxes with those published in  Leroy et al. (2009)  for available galaxies, finding relative differences to be as small as 5-10%. To study the dust SED and ISRF of these galaxies, we further collected multi-wavelength photometry data in the literature. In our sample, 22, 15, 7 and 6 galaxies have Herschel far-IR photometry from  Chu et al. (2017) ,  Dale et al. (2017) ,  Clark et al. (2018)  and  Clements et al. (2018) , respectively. Eight have SCUBA2 850 µm photometry from  Lisenfeld et al. (2000). Note that  Dale et al. (2017)  provide the full UV/optical-to-infrared/sub-mm SEDs 7. All of these local galaxies have Herschel PACS 70 or 100 µm and 160 µm photometry from L15. Fluxes are consistent among these works. For example, comparing L15 with  Chu et al. (2017) , we find 13 galaxies in common, and their median flux ratio in logarithm is -0.
01 dex, with a scatter of 0. 04 dex. For our SED fitting, we average all available fluxes for each band. In addition, we cross-matched with  Brown et al. (2014) ,  Jarrett et al. (2003) ,  Brauher et al. (2008) , and the NASA Extra-galactic Database (NED) for missing optical to near-/mid-infrared photometry. All local galaxies have 2MASS near-IR photometry from  Jarrett et al. (2003)  except for NGC 2369 and NGC 3256. For 9 galaxies which do not have any optical photometry from  Dale et al. (2017)  and  Brown et al. (2014) , we use the optical/near-/mid-IR photometry from NED 8.
High-z SB FMOS galaxies with new PdBI observations We observed the CO(5-4) line emission in six z ∼ 1. 6 starburst galaxies from the FMOS-COSMOS survey  (Silverman et al. 2015b)  with IRAM PdBI in the winter of 2014 (program ID W14DS). These galaxies have ALMA CO(2-1) observations presented in  Silverman et al. (2015a). Our PdBI observations are at 1. 3 mm. Phase centers are set to the ALMA CO(2-1) emission peak position for each galaxy, and the on-source integration time is 1. 5 to 3. 1 hrs per source. Sensitivity is 0. 6-0. 7 mJy/beam over the expected line widths of 200-600 MHz, depending on the ALMA CO(2-1) line properties of each source. With robust weighting (robust factor 1), the cleaned images have synthesized beam FWHM of 2. 0-3. 3. As the ALMA CO(2-1) data have much higher S/N than the PdBI CO(5-4) data, we extract the CO(5-4) line fluxes in the uv plane by Gaussian source fitting with fixed CO(2-1) positions and line widths (from  Silverman et al.
(2018 ), and Harrington et al. (2018 , 2019) , despite the fairly good sampling of their CO SLEDs. Their strong magnification ( 10) largely reduces the observing time (×1/100) for CO observations compared to unlensed targets. Yet their optical to mid-IR SEDs are usually not well sampled. Harrington et al. (2020)  present a study of CO excitation and D. Liu et al. far-IR/(sub-)mm dust SED modeling in strongly lensed galaxies, based on a similar gas density PDF modeling. Among our SMG subsample, GN20 is in the GOODS-North field, and AzTEC-3 and COSBO11 are in the COSMOS field. They have rich multi-wavelength photometry as mentioned earlier. Tan et al. (2014)  fitted the GN20 SED with DL07 templates without an AGN component, and our new fitting to the same photometry data shows that a mid-IR AGN component is indistinguishable from the warm dust component in the DL07 models.
T HIS S15 PACS-164 high-z SB FMOS 1. 65 1. 9 ± 0. 4 2. 4 ± 0. 7 18. 1 +35. 0   -15. 4   12. 5 +0. 0 -0. 0 10. 2 +0. 3 -0. 2 T HIS S15 V20-ID41458 high-z SB V20 1. 29 1. 8 ± 0. 2 2. 4 ± 0. 3 33. 5 +11. 0 +0. 0 12. 5 +0. 0 -0. 0 11. 1 +0. 0 -0. 0 V 20 V 20 V20-ID21060 high-z SB V20 1. 28 3. 6 ± 0. 9 3. 4 ± 1. 3 51. 5 +0. 5 -1. 5 12. 3 +0. 0 -0. 0 10. 0 +0. 1 -0. 0 V 20 V 20 V20-ID51599 high-z SB V20 1. 17 2. 1 ± 0. 2 2. 5 ± 0. 4 14. 4 +3. 7 -1. 9 12. 5 +0. 0 Note-Only a few selected key columns are shown here. The full sample table has more columns including galaxy properties of DL07 warm and cold dust luminosities, AGN luminosities, offset from the MS, which are used in Fig. 2. The full machine-readable table is available at https://doi. org/10. 5281/zenodo. 3958271. -0. 0 11. 1 +0. 1 -0. 0 V 20 V 20 V20-ID30694 high-z MS V20 1.
Comparing with popular panchromatic (UV-to-mm/radio) SED fitting codes, e. g. ,  MAGPHYS (da Cunha et al. 2008 , 2015) , LePhare  (Arnouts et al. 1999; Ilbert et al. 2006) , CIGALE  (Noll et al. 2009; Ciesla et al. 2015; Boquien et al. 2019) , our code fits SEDs well and produces similar best-fitting results (see Appendix C). Our code also performs χ 2 -based posterior probability distribution analysis and estimates reasonable (asymmetric) uncertainties for each free or derived parameter (e. g. , Fig. 1 ). Our code can also handle an arbitrary number of SED libraries as the components of the whole SED. For example, we use five SED libraries/components representing stellar, AGN, DL07 warm dust, DL07 cold dust, and radio emissions (see below). Our code samples their combinations in the five-dimensional space, then generates a composite SED (after multiplying the model with the filter curves), then fits to the observed photometric data and obtains χ 2 statistics.
online version). To verify our SED fitting, we also fit our high-z galaxies' SEDs with MAGPHYS and CIGALE (see more details in Appendix C). We find that for most highz galaxies the stellar masses and IR luminosities are agreed within ∼ 0. 2 -0. 3 dex. The IR luminosities are more consistent than stellar masses among the results of three fitting codes, with a scatter of ∼ 0. 2 dex. In several outlier cases, our code produces more reasonable fitting to the data (e. g. , AzTEC-3, Arp220, NGC0253), which is likely because we do not have an energy balance constraint in the code. Our code has no systematic bias against CIGALE, but there is a noticeable trend that MAGPHYS fits slightly larger stellar masses than the other two. A possible reason is the use of the  Charlot & Fall (2000)  double attenuation law in MAGPHYS (see Lo  Faro et al.
2017 ) rather than the  Calzetti et al. (2000)  attenuation law in our MiChi2 and CIGALE fitting. Given the general agreement between our code and CIGALE/MAGPHYS, and to be consistent within this paper, we fit all SEDs with our MiChi2 SED fitting code with the five SED libraries as mentioned above. We use our SED fitting results and the compiled CO data to study the empirical correlation between the CO(5-4)/CO(2-1) line ratio R 52 and the mean ISRF intensity U. This correlation physically links molecular gas and dust properties together, supporting the idea that gas and dust are generally mixed together at large scales and exposed to the same local ISRF. In Fig. 2  we correlate R 52 with various galaxy properties derived from our SED fitting. Panel (a) shows a tight correlation between R 52 and the ambient ISRF intensity U min , and panel (b) confirms the tight correlation between R 52 and U which was first reported by  Daddi et al.
Indeed, sub-samples behave slightly differently in Fig. 2. While local SFGs and local (U)LIRGs are scattered well around the average R 52 -U correlation line, high-z MS and SB galaxies from the FMOS and V20 subsamples tend to lie below it. Given the varied S/N of IR data as reflected by the U error bars, the majority of those high-z galaxies do not have a high-quality constraint on U. High-z sample selections for CO observations are usually also biased to high-z IR-bright galaxies. Therefore, it is difficult to draw a conclusion about any redshift evolution of the R 52 -U correlation with the current dataset. From panel (f) of Fig. 2 , we can see that there are several galaxies showing a high AGN to ISM dust luminosity ratio (note that the AGN luminosity is integrated over all wavelengths, while the IR luminosity is only DL07 warm+cold dust integrated over 8-1000µm).
(2017, 2019)  and  Valentino et al. (2020b)  also found positive correlation between the gas kinetic temperature and dust temperature which is proportional to U 0. 16. There is also a weak trend that T kin increases with SFR/SFR MS (Pearson correlation coefficient P = 0. 34), and the trend between n H2 and SFR/SFR MS is also marginal (P = 0. 40). Given these results, it is very reasonable that both mean gas density and temperature increase from less to more intensively star-forming galaxies. Yet based on the datasets in this work, it is difficult to statis- tically decouple n H2 and T kin and hence to measure well the shapes of Un H2 and U -T kin correlations. However, the non-linear or broken Un H2 correlation and the more smooth U -T kin might imply two scenarios, one for "normal" star-forming galaxies, and one for merger-driven starbursts.
Limitations and outlook We discuss three limitations of this work: the overall quality of current datasets, the assumptions in the gas modeling, and the contamination from AGN. First, CO line ratio or SLED studies require two or more CO line observations. These observations have different observing conditions, beam sizes, flux calibrations, etc. , thus uncertainties are very likely underestimated even when the S/N of the line measurements are formally large (e. g. , > 3). For example, for our local SFG subsample, CO(5-4) data are from the Herschel FTS with a certain beam size of ∼ 40 , which does not match the mapping area of CO(2-1) from ground-based telescope. The correction from the FTS beam to the entire galaxy can have a factor of two difference, which is reflected in the scatter of our data points although not fully reflected in their errorbars.
The absolute flux calibration uncertainty of the observations in the literature can also be as high as ∼ 30%, which is much poorer than current IRAM 30m and ALMA (total power) observations (< 10%). This also increases the scatter in our plots and necessarily makes observed correlations less significant. As for high-redshift galaxies, we use a S/N of 3 in both two CO lines to select our sample, which usually only reflects the quality of line measurements while it does not include the absolute flux calibration uncertainty. Their dust SEDs are also much more poorly covered, thus their U have fairly large uncertainties. Future ALMA Band 3 to 8 mapping of CO lines from J u = 1 to 4 in local galaxies, and VLA plus ALMA observations for suitable galaxies at high redshift with high-quality CO and continuum data will be the key to both spatially understand and statistically verify correlations between U , n H2 and T kin , as well as to unveil any evolutionary trend with redshift.
B. SOME NOTES ON CO OBSERVATIONS OF INDIVIDUAL NEARBY GALAXIES IN THE LITERATURE CenA: We excluded this galaxy because its CO(2-1) and CO(5-4) data only cover the center of the galaxy, and significant correction is needed for recovering the entire galaxy. For example, Kamenetzky et al. (  2014 ) applied a correction factor of 1/0. 48, where 0. 48 is the beam-aperture-to-entire-galaxy fraction denoted as "BeamFrac" and reported in the full Table  1  (available online), to convert the CO(2-1) observed at the galaxy center with a beam of 22  (Eckart et al. 1990b ) to a beam of 43 for their study. They derived this factor based on SPIRE 250 µm image aperture photometry. Based on PACS 70 -160 µm (as presented in L15), we obtain correction factors of 1/0. 088 and 1/0. 185 from a 22 and 43 beam to the entire galaxy, respectively.
full Table 1 are publicly available at: https:// doi. org/ 10. 5281/ zenodo. 3958271. ACKNOWLEDGMENTS We thank the anonymous referee for helpful com- ments. DL, ES and TS acknowledge funding from the European Research Council (ERC) under the Euro- pean Union's Horizon 2020 research and innovation pro- gramme (grant agreement No. 694343). GEM acknowl- edges the Villum Fonden research grant 13160 "Gas to stars, stars to dust: tracing star formation across cosmic time" and the Cosmic Dawn Center of Excel- lence funded by the Danish National Research Founda- tion under then grant No. 140. YG's research is sup- ported by National Key Basic Research and Develop- ment Program of China (grant No. 2017YFA0402700), National Natural Science Foundation of China (grant Nos. 11861131007, 11420101002), and Chinese Academy of Sciences Key Research Program of Frontier Sci- ences (grant No.
QYZDJSSW-SLH008). SJ acknowl- edges financial support from the Spanish Ministry of Science, Innovation and Universities (MICIU) under grant AYA2017-84061-P, co-financed by FEDER (Euro- pean Regional Development Funds). AP gratefully ac- knowledges financial support from STFC through grants ST/T000244/1 and ST/P000541/1. We thank A. Weiss and C. Wilson for helpful discussions. This work used observations carried out under project number W14DS with the IRAM Plateau de Bure Interferometer (PdBI). IRAM is supported by INSU/CNRS (France), MPG (Germany) and IGN (Spain). This work used obser- vations carried out under project 17A-233 with the Na- tional Radio Astronomy Observatory's Karl G. Jansky Very Large Array (VLA). The National Radio Astron- omy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
Participants One hundred and twenty five healthy volunteers, native speakers of Polish, 52 righthanders [mean age (M A ) ¼ 22. 1, standard deviation (SD) ¼ 1. 9], 31 mixedhanders (M A ¼ 22. 9, SD ¼ 4. 2), and 42 lefthanders (M A ¼ 23. 1, SD ¼ 3. 2), typically students of, or graduates from, Pozna n universities, were tested in two consecutive, larger fMRI projects. Volunteers were required to be healthy adults, between the age of 18 and 40. Exclusion criteria involved no medically documented history of neurological or psychiatric disorders, no contraindications to undergoing MRI, and no obvious brain malformations (visible in the obtained scans). These criteria were established prior to data acquisition, their analyses, all manipulations, and all measures used in our projects. Only praxis and language neuroimaging data from the obtained three samples were used in this report.