dataesr/scientific-paragraphs-categorization huggingface dataset:
A first estimate of the illumination pattern B 0 is obtained assuming a uniform camera response M 0 = 1, and this B 0 is used to solve for a first map of the camera response M 1. The procedure is iterated once and quickly converges. The model is an accurate description of the dataset except for projective features such as defects on the camera window. The resulting camera response maps are shown in Fig. 16. All demonstrate uniformity in the response at the level of 4% peakto-peak (<1% RMS), with the position selected for the QE measurement generally being slightly below the average. The pattern shows some evolution with wavelength; however, the evolution appears slow enough to be corrected by filter-dependent flat-fielding, with an accuracy dependent on the chosen photometric system.
Acknowledgements. The authors thank the  QHY company  for providing the camera tested in this study and for kindly answering our questions. Warm thanks are also addressed to  Pierre Astier  and  Pierre Antilogus  for thoughtful discussions and comments on DNL issues, and to  Parker Fragelius  and  Elana Urbach  for their help in improving the text. This work received support from the  Programme National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES  and from the  DIM ACAV program  of the  Île-de-France region.
Tailored and universal pTx pulse design For in vitro experiments, tailored pulses (TP) were designed specifically for the phantom used. Following a B 0 measurement performed using a three-dimensional-GRE protocol, a B + 1 map was acquired with an in-house made interferometric turbo-FLASH sequence called XFL  34  (5 mm isotropic resolution, matrix size 40 × 64 × 40). This B + 1 map was used for the optimization in Equation (5). For in vivo acquisitions, TPs were designed for each volunteer following an identical procedure. In addition, UPs were also used for the in vivo acquisitions. The use of UPs presents the advantage of an offline design while still offering excellent B + 1 uniformization capability, comparable to TPs. 29  For designing UPs a composite matrix b 1 (r) was built by concatenating B + 1 maps from a large database F I G U R E 1 Diagram of the chemical exchange saturation transfer pulse sequence showing the events during one repetition time.
The colored pulses, A and B, are pairs of parallel transmission pulses. Each pair is played N times before spoiling and echo-planar imaging readout to produce an effective saturation time of T sat = 2N × T pulse. of subjects. Our database consists of 26 subjects,  35  with 13 randomly selected maps serving as optimization group and 13 others as evaluation group. The pulse optimization was done in MATLAB (Mathworks, version 9. 7, R2019b) using an interior-point solving algorithm (Intel Core i7-9850H CPU at 2. 60 GHz, 32. 0 Go RAM). For TPs, calculated using only one B + 1 map, the average computational time was 13 s, while for UPs, calculated using 13 B + 1 maps, it was 22 s.
5 s. For the exams performed on volunteers, an anatomical image, necessary for segmentation, was also acquired using an MP2RAGE sequence  36   Following the above-described protocols, hydroxyl-weighted CEST acquisitions were performed using CP, MIMOSA, and optimized pTx pulses (with TPs for phantom and UPs in vivo). If the SAR constraints allowed it, the spatially averaged saturation RF amplitude, B + 1,RMS , of MIMOSA and CP were kept at the same level as the UPs (made on the evaluation dataset). Otherwise, the magnitude of MIMOSA and CP pulses was scaled down to reach the maximum allowed SAR (see Table  S1 ). The two MIMOSA modes had the same amplitude. The TP and UP pulses were optimized on a 10 mm-thick slab, centered on the imaged slice. In vivo glutamate-weighted CEST acquisitions were also performed but only using pTx with UPs.
Data analysis A retrospective motion correction (image to image) was performed on in vivo CEST acquisitions using SPM12. An extrapolation of movement for frequencies between -0. 75 and 0. 75 ppm was done to avoid registration of low-intensity images. After the motion correction, a B 0 correction was made using the water saturation shift referencing acquisition. Specifically, the water peak was fitted with a Lorentzian curve and the acquired Z spectra to be corrected were linearly interpolated and shifted by the same value as that required to bring the center of the fitted Lorentzian curve to 0 ppm. To produce various CEST weighted maps, the Magnetization Transfer Asymmetry (MTR asym ) was computed voxel by voxel according to: MTR asym (Δ𝜔) = S(-Δ𝜔) -S(Δ𝜔) S ref. ( 6 ) For each volunteer, we produced glutamate and hydroxyl-weighted CEST maps as well as an rNOE map (using the hydroxyl-weighted CEST acquisition as saturation parameters are close  3, 14  ) by calculating MTR asym at Δ of 3 ppm, 1.
Pulse scheme efficiency The first pulse optimization result was that increasing the number of modes beyond two does not significantly improve B + 1,RMS homogeneity, as observed previously by Leitão et al. 28  We therefore only focused on the optimization of two modes. Typical TPs optimized for hydroxylweighted CEST acquistions for a given volunteer are shown in Figure  2A. While the optimization does not result in a trivial solution, we note that, due to the combined optimization of the two modes, the spatial distributions of the B + 1,RMS show spatial complementarity (Figure  2B ). B + 1,RMS maps obtained on the evaluation dataset using Equation (3) for both UPs and CP are shown in Figure  S2  (the optimization was performed using hydroxyl-weighted CEST presaturation parameters). UPs provide a much more homogeneous saturation than CP.
This study aims to enable endogenous glucose weighted CEST imaging. While our in vivo data is supported both by in vitro and simulation results, the MTR asym maps presented reflect several CEST signals. Robust separation of different metabolic contributions remains a major challenge for CEST imaging and it is beyond the scope of this paper. The presented approach can also be used to perform dynamic glucose  42, 43  studies. Moreover, by acquiring data only at strategically chosen frequency offsets in order to minimize the acquisition time, metabolic functional imaging studies, similar to those performed in rodents,  44  are conceivable.
These test results provide a datasets of a, b, and c that will be used subsequently according to the parameters of the scenario at hands. If A m and A p are equal, the power P m,dBm and P p,dBm should be equal. However, in practice, we have observed that by assigning the same numerical value to A m and A p , P m,dBm and P p,dBm through the power sensors are different. This difference between P m,dBm and P p,dBm can be adjusted and compensated in baseband by using a parameter denoted by ψ with P m,dBm (A m ) = P p,dBm (A p + ψ ) ( 6 ) Finding ψ that satisfies (  6 ) can be done in the calibration process. However, we propose to consider it as a free parameter controlled from baseband, which could be viewed as a hardware parameter since it can adjust the input power distribution over the main and peaking amplifiers.
A. DESIGN OF COST FUNCTION The cost function is an essential aspect in this research work to ensure a good trade-off between linearity and efficiency. The linearity requirement is presented in terms of two figures of merit (FOMs): error vector magnitude (EVM) and ACPR, while the efficiency requirement is presented by PAE and the output power P out. The EVM is a metric that measures the in-band distortion level. It is defined in the constellation domain and evaluates the deviation between the reference constellation point and the actual constellation point obtained in the presence of distortions. Analytically, EVM is defined as: EVM % = 1 N N-1 j=0 (δI 2 j + δQ 2 j ) S 2 avg × 100% (7) Where δI and δQ are errors magnitude corresponding to in-phase symbol and quadrature symbol of received data compared with an ideally reconstructed constellation respectively, N is the number of symbols, S 2 avg is the average square magnitude.
IV. TEST BENCH In order to validate the effectiveness of the proposed system approach, experiments have been carried out using a test bench. The block diagram and the photo of the test bench are shown in Figs. 6  and 7 , respectively. The DIDPA is controlled and evaluated using a MATLAB-based linearization and efficiency enhancement technique. The baseband IQ data are generated and split into two different IQ data inputs sent to the DUT through the AD9371 dual-channel RF transceiver, which is connected to Xilinx FPGA ZC700 through FMC connectors. The transceiver AD9371 up-converts the baseband signals to the carrier frequency f c at 3 GHz. A Keysight N9010A MXA spectrum analyzer is used to characterize the signals spectrum at the output of DUT. For the observation path, The RF output signal is down-converted to the baseband by AD9371, which provides the baseband signal to the PC workstation.
, 2021;; Boselli et al. 2021; Roberts et al. 2022a). The structure of this paper is as follows: Sect. 2 describes the data products used in this work and outlines the method that we use to quantify gas asymmetries. Section 3 presents a comparison between atomic and molecular gas asymmetries in Virgo galaxies and our control sample. Section 4 tests whether tailed galaxies show excess molecular gas emission on their leading halves. Section 5 provides a summary of the primary results and a brief discussion. Throughout this paper we assume a common distance of 16. 5 Mpc to all Virgo galaxies  (Mei et al. 2007 ) and a Chabrier initial mass function  (Chabrier 2003).
VERTICO molecular gas data VERTICO  (Brown et al. 2021 ) is an ALMA large programme that uses the Atacama Compact Array (ACA) to obtain kiloparsec-scale imaging of the 12 CO(2-1), hereafter CO, line for the aforementioned 51 Virgo cluster galaxies. Of these galaxies, 15 already had archival CO observations from the ACA (2015. 1. 00956. S: PI A. Leroy, 2016. 1. 00912. S: PI J. Kenney, 2017. 1. 00886. L: PI E. Schinnerer) and the remaining 36 were observed as part of the VERTICO programme in ALMA Cycle 7. Of the 36 VERTICO galaxies observed in ALMA Cycle 7, total power observations were added for 25 galaxies that were expected to have emission on scales larger than the largest angular scale of 29 for the ACA in ALMA Band 6. Total power data were also available for 14/15 of the archival VERTICO galaxies.
There are comparatively few UV-bright foreground stars that require masking in the SFR maps, though this is in part due to the wavelength coverage of Gaia. Star maps are also inspected by eye, and when required, bright stars missed by this automated masking are masked by hand. We used the stellar mass map for each galaxy to define the galaxy centre about which gas asymmetries are measured (see Sect. 2. 4), and for splitting H i-tailed galaxies into leading and trailing halves (see Sect. 4). We define the galaxy centre to be the centroid of the stellar mass map using the centroids module from the Python package photutils. Our adopted galaxy centres are determined from a 'centre-of-mass' centroid estimator (photutils. centroids centroid_com) applied to the stellar mass maps, though we also find qualitatively similar results using a 2D Gaussian centroid estimator (photuils.
We considered a quantitative measure of the difference in H 2 mass between the leading and trailing halves, δ H 2 ≡ M H 2 , LH M H 2 , TH , (5) where M H 2 , LH and M H 2 , TH are the total H 2 gas masses on the leading and trailing halves, respectively. We also define four analogous quantities in terms of H i mass, SFR, molecular gas depletion time (t dep ), and stellar mass given by δ H i ≡ M H i, LH M H i, TH , (6) δ SFR ≡ SFR LH SFR TH , (7) δ t dep ≡ M H 2 , LH /SFR LH M H 2 , TH /SFR TH , (8) and δ ≡ log M , LH M , TH. (9) When calculating these delta quantities, we only include pixels within the optical galaxy radius, taken to be r 25 from the Hyperleda database 5   (Makarov et al. 2014 ). Thus, this analysis focuses on anisotropies within the galaxy disc, which is where signatures from potential gas compression are expected to occur.
Solid lines and shaded bands mark the median and statistical error on the median. Both errors on the median and error bars on the data points are derived from bootstrap resampling. Both H 2 and SFR show excess emission on the leading half. Typically, by a factor of ∼10% and up to ∼50% for some galaxies. Conversely, these galaxies also have truncated H i emission on their leading halves compared to their trailing halves (a product of gas stripping), and thus the combined effect leads to a total atomic gas mass that similar between the two halves. SFR leading half anisotropy versus that for the molecular gas mass. The clear correlation highlights that galaxies with large leading half excesses of molecular gas also show similar excesses in star formation. The one notable outlier to the trend is NGC 4396.
Acknowledgements. The authors thank the anonymous referee for providing a timely and constructive report, which has resulted in an improved manuscript. We also thank  Seona Lee  for sharing the H i tail orientations for the Virgo galaxies in this work. This work was carried out as part of the VERTICO collaboration. IDR acknowledges support from the  Natural Sciences and Engineering Research Council of Canada  and the  ERC Starting Grant  Cluster Web  804208  (PI,  R. J. van Weeren ). TB acknowledges support from the  National Research Council of Canada  via the  Plaskett Fellowship of the Dominion Astrophysical Observatory. The financial assistance of the  National Research Foundation (NRF)  towards this research is hereby acknowledged by NZ. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF.
NZ is supported through the  South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation. CDW acknowledges support from the  Natural Sciences and Engineering Research Council of Canada  and the  Canada Research Chairs program. LCP and KS acknowledge support from the  Natural Sciences and Engineering Research Council of Canada. ABW and LC acknowledge support from the  Australian Research Council Discovery Project  funding scheme ( DP210100337 ). TAD acknowledges support from the  UK Science and Technology Facilities Council  through grants  ST/S00033X/1  and  ST/W000830/1. BL acknowledges the support from the  Korea Astronomy and Space Science Institute  grant funded by the  Korea government (MSIT)  (Project No. 2022-1-840-05 ). ARHS acknowledges receipt of the Jim Buckee Fellowship at  ICRAR-UWA.
VV acknowledges support from the scholarship  ANID-FULBRIGHT BIO 2016 -56160020 , funding from  NRAO Student Observing Support (SOS)   -SOSPA7-014 , and partial support from  NSF -AST2108140. Parts of this research were supported by the  Australian Research Council Centre of Excellence  for All Sky Astrophysics in 3 Dimensions ( ASTRO 3D ), through project number  CE170100013. We acknowledge the usage of the HyperLeda database (http: //leda. univ-lyon1. fr). This paper makes use of the following ALMA data: ADS/JAO. ALMA #2019. 1. 00763. L  ADS/JAO. ALMA # 2017. 1. 00886. L. ADS/JAO. ALMA  # 2016. 1. 00912. S ADS/JAO. ALMA  # 2015. 1. 00956. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
However, the authors also recorded pupil dilations during task performance and reported both interference and negative facilitation in this metric (pupils were smaller for the repeated letter string condition than for congruent stimuli). The timeseries data showed that pupil data began to distinguish between the repeated letter string condition and incongruent and congruent conditions up to 500ms before there was divergence between the incongruent and congruent trials. More recently,  Hershman et al. (2021)  reported that, in terms of colour naming response times, repeated letter strings did not differ relative to symbol strings (e. g. , %&^$), consonant strings (e. g. , CGHD) and colour-neutral words (e. g. ,  table ). All were responded to more slowly than congruent trials evidencing positive facilitation on congruent trials.
Psychiatric disorders include a broad panel of heterogeneous conditions. Among the most severe psychiatric diseases, in intensity and incidence, depression will affect 15-20% of the population in their lifetime, schizophrenia 0. 7-1%, and bipolar disorder 1-2. 5%. Today, the diagnosis is solely based on clinical evaluation, causing major issues since it is subjective and as different diseases can present similar symptoms. These limitations in diagnosis lead to limitations in the classification of psychiatric diseases and treatments. There is therefore a great need for new biomarkers, usable at an individual level. Among them, magnetic resonance imaging (MRI) allows to measure potential brain abnormalities in patients with psychiatric disorders. This creates datasets with high dimensionality and very subtle variations between healthy subjects and patients, making machine and statistical learning ideal tools to extract biomarkers from these data.
Inter-Illness Classification and Clustering One major challenge of machine learning studies using MRI is to be able to correctly distinguish or classify patients suffering from different disorders. Several studies focused on the classification between BD and SZ. In their review, Claude et al. [54]  found that three studies used sMRI in combination with machine learning algorithms to discriminate between BD and SZ with an accuracy ranging between 58% and 66%. Precisely, Schnack et al. [44]  showed good classification performance on an independent dataset, with an average classification accuracy of 66%. Mothi et al. [45]  used K-mean clustering after a non-linear PCA to separate patients with BD, SZ, or schizoaffective disorder. They found out that the separation in three clusters was optimal, comprising a cluster including a major proportion of patients with BD, a second with mostly patients with SZ, and a third with a balanced proportion of the three types of illnesses.
As it is difficult to recruit enough patients to obtain a sufficient statistical power, this limitation may persist in the long term, unless collective efforts for data sharing are undertaken. This issue deepens when looking at more specific subsets of patients. The field therefore needs more and larger datasets to work on. These datasets start to be collected, with, e. g. , the UK BioBank dataset (~40,000 subjects). Even though they are not focused on psychiatric disorders, they are interesting because they are multimodal datasets, with genetic, clinical, and MRI data, and some participants will develop psychiatric syndromes throughout the follow-up. Recent efforts have been specifically made for psychiatric disorders, e. g. , by the ENIGMA Consortium, a multisite and multimodal project including several working groups focused on different diseases, such as bipolar disorder, schizophrenia, autism, ADHD, etc.
Larger datasets are often multisite ones, and they bring their own challenges. Since the MRI devices that are used for different studies have different magnetic field strengths, different vendors, coils, etc. , there are large site effects that need to be considered. These site effects are particularly important for DWI and fMRI, but they even appear for sMRI  [82] , the most robust method of imaging. A second source of site effects lies in the preprocessing of the data, which may vary between different sites and protocols. The preprocessing steps are of major importance and need to be homogenized since different softwares can lead to different results  [83]. The remaining "site effects" can be partially corrected, thanks to different methods. Statistics-based methods include adjusted residualizations or ComBat  [84, 85] , a method originally proposed to remove batch effects in genomics  [86]  and then adapted for DWI and then for sMRI  [87].
Other methods are more specific to MRI, such as RAVEL  [88] , which aims at capturing the sites' variability using the signal from the CSF, with mixed results for now. Since the extent of the efficiency of these corrections is still under discussion  [89] , we must consider the site effect in our models and use validation methods such as leave-one-site-out validation to evaluate the reproducibility of our approaches. The site effect highlights a deeper and more fundamental limitation of our studies, the signal-to-noise ratio. That issue, which is faced by all imaging studies, is particularly present in neuroimaging for psychiatric diseases as the changes that we are looking for are subtle and probably not the main causes of variation in our datasets (e. g. , one important cause of variance is the age, which produces consequent variations in the gray and white matter density  [72] ).
We therefore need to be vigilant and make specific efforts when interpreting the results of machine learning algorithms as they can learn some information that are irrelevant for psychiatric disorders. Nevertheless, it is possible to improve this signal-to-noise ratio. One way to do so is to improve the signal; the second is to diminish the noise. Larger datasets improve the statistical power of the algorithms but may induce noise (such as the multisite noise). In addition to the fact that methodological modifications can change and improve the performance of machine learning, technological improvements seem to bring better performance as shown by the team of Iwabuchi et al. [90] , who showed that 7 T MRI compared to 3 T MRI gave higher classification accuracy when distinguishing patients with schizophrenia vs.
controls (77% versus 66%). Moreover, the use of multimodal datasets has shown promising results in increasing the signal-to-noise ratio in current studies  [91]. While trying to determine to what extent machine learning using MRI can still improve its results, Schulz et al. [91]  highlighted two interesting perspectives: first, that there is still room for improvement of the classification accuracy by getting larger datasets and second, that multimodal MRI and more specifically fMRI could improve the classification. Other ways to collect data could also be thought about, with, for example, the use of tools such as smartphones. Data can be provided through active monitoring (self-reporting), passive monitoring of various activities, mobility, or statistics on phone calls  [92]. Promising results show that voice data from daily phone calls could be a valid marker of mood states and hold promise for monitoring BD  [93].
Study population This analysis is based on pooled patient-level data of BACC (Biomarkers in Acute Cardiac Care; NCT02355457)  [9]  and APACE (Advantageous Predictors of Acute Coronary Syndromes Evaluation; NCT00470587) observational studies  [10, 11]. In both studies, patients presenting to the ED with symptoms suggestive of MI (such as acute chest discomfort) with an onset or peak within the last 12 h and an age ≥ 18 were recruited. For this analysis, patients with STEMI or Type 4 MI as well as patients with an unknown diagnosis after final adjudication were excluded. For the calculation of the ESC 0/1 h-algorithm, patients with missing 1 h hs-cTnT concentrations were further excluded (Online Fig. 1 ). All patients underwent routine clinical assessment including medical history, physical examination, standard blood tests including serial measurements of local hs-cTnT, 12-lead electrocardiogram (ECG), chest radiography (if requested), continuous ECG rhythm monitoring, and pulse oximetry.