dataesr/scientific-paragraphs-categorization huggingface dataset:
The current analysis is limited by the lack of a control group and a high risk of selection bias. The data collected at 1 month before LNG-IUS insertion could be highly variable owing to individual situations. Baseline values may require adjustment to assess MDQ and MMAS score changes more accurately; however, not enough data were collected, and thus the true baseline values remain unknown. While improvements in VAS, PBAC, MDQ, and MMAS scores were observed, whether improvements in VAS and PBAC scores directly led to improvements in MDQ and MMAS remains debatable. Finally, whether the MDQ and MMAS scores could further improve beyond the present study's 12-month observation period is currently unknown, and long-term 5-year follow-up studies are warranted in the future to con rm this.
Acknowledgements: This study was supported by  Bayer Yakuhin, Ltd. The authors would like to thank  Michelle Belanger, MD , of  Edanz  (www. edanz. com), for providing medical writing support, which was funded by  Bayer Yakuhin, Ltd. through EMC K. K. in accordance with Good Publication Practice (GPP3) guidelines (http://www. ismpp. org/gpp3). The authors would also like to thank  Dr. Yoshimi Suzukamo , of  Tohoku University , for providing insightful advice on analysis methods and interpretation of the results.
Once the solution was filled, the top and bottom were closed and the water in the glass tube outside the membrane was kept at constant height by attaching it to an external water reservoir (using a conical flask). A schematic figure of the setup is shown in supplementary figure S1. The movement of the sugar solution was recorded using a digital camera (Sauledo 4K, 60FPS, 48MP video recorder) acquiring images every 30 s. To ensure a reference color for later image processing, a white background was set using a white paper attached to the glass. Pressure data from the transducer was collected every 30 seconds as well.
Data processing Image processing tools in MATLAB programming language (Mathworks, Natick, MA) were used to detect simultaneously the front location and membrane diameter for the experiments. These data were post processed by removing the initial period (1-2 hours) that was noisy because of the inertial effects at the start of the experiment (dextran transient mixing before osmosis building up). In addition, the final time that was used to obtain the results was chosen to be the time when the pressure reached 90 % of its equilibrium value, or when the front reached the top of the membrane in case of high initial concentration. This period represented mainly the elastic regime and the initial part of the plastic regime before any membrane failure occurred after prolonged stress exposure. All in all, the study yielded 28 runs when combining both dextrans.
Funding This work was supported by the  U. S. National Science Foundation  [ NSF-IOS-1754893 ,  NSF-AGS-2028633 ], the  Department of Energy  [ DE-SC0022072 ] and Los Alamos Directed Research and  Development Exploratory Research Grant  [No. 2020109DR ]. Data accessibility: Data is available on Duke Research Data Repository (https://doi. org/10. 7924/r45d8zp3s).
C. Validation We generated DFT data for FCC and BCC aluminium, and followed the procedure outlined above to construct ACE models for the Hamiltonian and overlap using several choices of basis sets. Full details of data generation, parameter estimation and prediction procedures are given in Methods (section IV). The ACE basis sets need to be carefully chosen for a particular application. The larger the basis, the higher the achievable accuracy, but larger basis sets also carry a risk of loss of transferability through overfitting. Each basis set is defined by three parameters: the correlation order ν and the maximum polynomial degrees n max , l max used in both the radial basis functions P nl (r) and the angular basis function Y lm ( x) of (  10 ) and  (12). In all our tests, the polynomial degrees are truncated in the manner of total degree, i.
e. , we let n + l ≤ d max for a given d max. For the onsite models, the body order is one more than the correlation order, i. e. ν = 1 corresponds to two body and ν = 2 to three body, while for the offsite models the body order is two more than the correlation order (since each term in the body order expansion depends on the bond in addition to ν particles from the environment). The offsite model has further flexibility in that one can choose different d max for bond and environment, say, d b max and d e max. To avoid overemphasising the impact of environment, we set d e max = d b max /2 in our implementation. We tested the accuracy of the fitted Hamiltonian and overlap matrices using different choices of these basis set parameters. The results are illustrated in Fig. 3. For the onsite blocks H II , we can obtain accurate and transferable results for all sub-blocks with correlation order ν = 2 (body order 3), with no significant overfitting as can be seen from the close agreement of prediction accuracies on the training and test datasets in Fig.
A. Data generation The datasets used in this work are constructed for face-centred cubic (FCC) and body-centred cubic (BCC) phases of Al. Our data was generated through electronic structure calculations with the all-electron numeric atomic orbital code FHI-aims (version 190530)  [25]. We used the Perdew-Burke-Enzerhof (PBE) generalized gradient approximation  [45]  to the exchange-correlation energy within the KS-DFT formulation, and neglected spin in our treatment. The convergence criteria for charge density, sum of eigenvalues, and total energy of the self-consistent cycles were set to 10 -5 e/a 3 0 , 5×10 -5 eV, and 10 -6 eV, respectively. The default tight FHI-aims basis set and integration grid definitions were used, which use a basis set confinement with a maximum radial basis function extent of 6 Å.
MD simuations for each phase were performed in the N P T ensemble using a 5 fs timestep and the Embedded Atom Method (EAM) potential proposed by Zhou et al. [47]. Single point DFT total energy calculations were carried out on the final configurations of each of these 500 MD simulations using FHI-aims with the parameters described above and a single k-point at Γ. We stored the resulting H and S matrices giving a dataset (H II , R I ) , (H IJ , r IJ , R IJ ) , (S IJ , r IJ , R IJ ). where II, IJ indicate on-and off-site blocks of the Hamiltonian and overlap matrices while r • and R • are the corresponding atomic structure data as defined in II A. For the onsite part for the Hamiltonian, we used 730 training and 728 test blocks and for the offsite part 2000 training and 1369 test blocks, with equal numbers of samples taken from the FCC and BCC MD data.
C. Prediction The software implementation of our method follows the workflow illustrated in Figure  1. The Julia packages ACE. jl  [48]  and ACEhamiltonians. jl implement the general Atomic Cluster Expansion basis sets and the specialisation to fitting and predicting Hamiltonians, respectively. Given an input configuration R we use the scheme described above to predict Hon (R), Hoff (R) and Soff (R). We then assemble complete approximate Cartesian Hamiltonian and overlap matrices H and S from the predicted blocks. We can construct k-dependent variants and associated bandstructures via a standalone Julia implementation contained within the ACEhamiltonians. jl package. Using either the reference or the predicted matrices we can solve the generalised eigenproblems of the form H(k)φ i = i S(k)φ i (20) H(k) φi = ˜ i S(k) φi  (21)  to obtain k-dependent band energies i , ˜ i and orbitals (eigenfunctions) φ i , φi for the reference and predicted systems respectively, where i = 1,.
Design This prospective inception cohort study was conducted in the Department of Thoracic Surgery, Rouen University Hospital, France. Consecutive adults scheduled for major lung resection via minimally invasive surgery were recruited between November 2018 and November 2019. Patients had a preoperative functional evaluation with the 6MST and STST. Complications graded 2 on the Clavien-Dindo classification were recorded for 90 days after surgery. The participants' results on the two preoperative exercise tests were analysed for their ability to predict postoperative complications. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were followed to report this study. 12 Privacy and confidentiality as well as the anonymity of the participants were ensured at all stages of data collection.
Follow-up and outcome measures Data on postoperative complications and their treatments were collected prospectively on day 30 during the postoperative consultation and on day 90 by analysis of hospital medical records, telephone contact with the patient and the attending physician if the patient suggested that there was a complication; the assessor (physiotherapist) was not always blind to the patient's performance on the baseline tests. All Clavien-Dindo grading from the complication description was performed by one independent physician who was blind to the patient's performance on the baseline tests. The primary outcome was the presence of at least one complication graded 2 on the Clavien-Dindo classification  19  occurring postoperatively up to day 90.
Phylogenetic analysis The taxonomic sampling used for the phylogenetic analyses included the 12 sequences newly generated for the study as well as one GenBank accession of Sarinda cutleri  (Richman, 1965)  (JX145669), a closely related species (see  Maddison et al. 2014 ) that served as the outgroup. All sequences were aligned in Geneious using MAFFT v. 7. 017  (Katoh & Standley 2013). The resulting COI matrix was subjected both to Bayesian inference (BI) and maximum likelihood (ML) analyses. We first searched for the best-fitting nucleotide substitution model using the Akaike information criterion (AIC) estimated by MrModeltest v. 2. 3  (Nylander 2004 );.
ACCEPTED MANUSCRIPT / CLEAN COPY the GTR + G model was selected and assigned to all following phylogenetic analyses. The software MrBayes 3. 2. 6  (Huelsenbeck & Ronquist 2001)  and RaxML-HPC v. 8. 1. 24 (Stamatakis 2014 ) implemented on the CIPRES Science Gateway  (Miller et al. 2010 ) platform were used to perform BI and ML analyses, respectively. MrBayes analyses constituted two independent parallel runs of four Markov chains each, implemented for one million generations and sampled every 100 generations. Adequate mixing of the Markov chains and convergence of the two runs were confirmed with Tracer v1. 6  (Rambaut et al. 2014 ). After removing a 10% burnin,the remaining trees were used to generate a 50% Bayesian majority-rule consensus tree. For the RaxML analyses, node support was assessed using a rapid bootstrapping algorithm with 1000 bootstrap iterations.
Acknowledgements The authors thank  Marguerite Delaval  ( Office National des Forêts, ONF ) and  ONF  for funding the expedition to the  Trinité National Nature Reserve ;  Christine Rollard  and  Christophe Hervé  ( Museum National d'Histoire Naturelle -Paris ) for allowing access to the bibliography of the  Arthropod laboratory  and permitting the consultation of Caporiacco and Simon's collection; and  Alain Canard ,  Boris Leroy ,  Frédéric Ysnel  and  Vincent Vedel  for field support;  Manuel Plantegenest ,  Yoann Navasse  and  El Aziz Djoudi  ( Agrocampus Ouest -Rennes ) for laboratory access and technical support.
Segmentations Whole-brain WMnMPRAGE volumes were processed with the THOMAS thalamic segmentation tool with no preprocessing. The THOMAS algorithm applied to WMnMPRAGE images has been validated against manual segmentation  (Su et al. , 2019). Using version v0 of this tool (https://github. com/sujason/thomas), we segmented and extracted the volumes of 12 lateralized structures in each hemisphere of the brain: whole thalamus, ten thalamic nuclei (anteroventral ), and one adjacent epithalamic structure, the habenula (Hb) (Fig. 1A ). Note that THOMAS segments the whole thalamus separately from the thalamic nuclei; this whole thalamus encompasses all these preceding structures, as well as the mammillothalamic tract and some additional unlabeled thalamic areas (i. e. , between segmented thalamic nuclei).
SAMSEG was shown to have the highest average Dice score of the five segmentation methods studied (SAMSEG, FreeSurfer, PICSL MALF, BrainFuse, and majority voting)  (Puonti et al. , 2016). Note that SAMSEG produces a whole thalamus segmentation, but we used the THOMAS segmentation of the whole thalamus because it includes the LGN and MGN, two major thalamic nuclei, whereas the SAMSEG whole thalamus does not. In addition, the thalamic contrast is higher in WMnMPRAGE images than in conventional T1-weighted images; thus we expect the THOMAS segmentation of thalamic boundaries to be more accurate. Intracranial volume (ICV) estimates were obtained from 3T whole-brain T1-weighted gradient echo volumes using SPM12's Segment and Tissue Volume Utility modules (https:// www. fil. ion. ucl. ac. uk/spm/software/spm12)  (Malone et al.
This approach was unavoidable, as no single method produces both sets of segmentations. Consequently, we note that there may be systematic software-specific effects that could affect comparisons between the atrophy rates of thalamic versus extra-thalamic structures. We note, however, that both THOMAS-derived and SAMSEG-derived whole thalamus volumes showed significant non-zero atrophy with age (0. 45% per year for THOMAS, 0. 23% per year for SAMSEG), indicating comparable volumetry for these two segmentation methods. Another caveat is that we compared brain structures of very differently sized volumes: the smallest structures we analyzed (habenula with an average volume of 25 mm 3 ) were almost 10,000 times smaller in volume than the largest structures (cerebral cortex with an average volume of nearly 250,000 mm 3 ).
g. , cerebral and cerebellar cortices) as a result of a higher degree of scatter in our cross-sectional dataset, combined with the non-uniform sample distribution. These limitations could be addressed in future longitudinal studies with more participants, ideally distributed uniformly over the lifespan, to better detect any potential nonlinearities in the age-related atrophy of thalamic nuclear volumes. Finally, the cross-sectional design adopted here is susceptible to variation in the volumes of these structures between individuals and does not provide a direct measure of withinindividual age-related atrophy. Nonetheless, the present findings provide important initial evidence for heterogenous rates of volume loss among thalamic nuclei across the lifespan, a pattern that should be confirmed in future longitudinal studies.
Acknowledgements We thank the following individuals for their contributions to data collection:  Jeff Bernstein ,  Nicole Corso ,  Wanjia Guo ,  Madison Hunt ,  Manasi Jayakumar ,  Anna Khazenzon ,  Celia Litovsky ,  Hannah Lloyd ,  Ayesha Nadiadwala ,  Natalie Tanner , and  Monica Thieu. We also thank  Dr. Garrett Drayna  for expert technical assistance and discussion. This work was supported by the  National Institutes of Health  ( P30 AG066515 ,  P41 EB01589125 ,  R01 AG048076 ,  R01 AG065255 ,  R21 AG058111 ,  R21 AG058859 ,  UH3 NS095554 ,  UL1 TR003142 ),  Department of Defense  ( W81XWH-20-PRARP-CSRA ),  Alzheimer's Association  ( AARFD-21-852597 ),  Stanford Precision Health and Integrated Diagnostic Center (PHIND) ,  Stanford Wu Tsai Neurosciences Institute, Stanford Center for Clinical and Translational Education and Research (Spectrum) ,  GE Healthcare, ARSEP Foundation , and  Idex Bordeaux University (IMPACT program ).
Conclusions This dataset comprehensively maps the dynamic functional characteristics of the two reading routes. Extant models suggest that all words should be initially processed through both reading pathways, with areas responsible for lexical access being activated inversely proportionally to lexical frequency, as demonstrated here in mFus and aIFG. Once a known word is detected, the sub-lexical route is interrupted (with activity returning to baseline level), and the word is named through the lexical route. However, if the word is novel, there are two possibilities: (1) that the lexical route is interrupted, with activity returning to baseline or (2) that the lexical route continues to attempt to identify the pseudoword while the sub-lexical route constructs the phonology of these words.
Electrode Implantation and Data Recording: Data were acquired from either subdural grid electrodes (SDEs; 4 patients) or stereotactically placed depth electrodes (sEEGs; 42 patients). SDEs were subdural platinum-iridium electrodes embedded in a silicone elastomer sheet (PMT Corporation; tophat design; 3mm diameter cortical contact), and were surgically implanted via a craniotomy  (Pieters et al. , 2013; Tandon, 2012; Tong et al. , 2020). sEEGs were implanted using a Robotic Surgical Assistant (ROSA; Medtech, Montpellier, France)  (Rollo et al. , 2020; Tandon et al. , 2019). Each sEEG probe (PMT corporation, Chanhassen, Minnesota) was 0. 8 mm in diameter and had 8-16 electrode contacts. Each contact was a platinum-iridium cylinder, 2. 0 mm in length and separated from the adjacent contact by 1.
5 -2. 43 mm. Each patient had 12-20 such probes implanted. Following surgical implantation, electrodes were localized by co-registration of pre-operative anatomical 3T MRI and post-operative CT scans in AFNI  (Cox, 1996). Electrode positions were projected onto a cortical surface model generated in FreeSurfer  (Dale et al. , 1999) , and displayed on the cortical surface model for visualization  (Pieters et al. , 2013). Intracranial data were collected during research experiments starting on the first day after electrode implantation for sEEGs and two days after implantation for SDEs. Data were digitized at 2 kHz using the NeuroPort recording system (Blackrock Microsystems, Salt Lake City, Utah), imported into Matlab, initially referenced to the white matter channel used as a reference for the clinical acquisition system and visually inspected for line noise, artifacts and epileptic activity.
2° visual angle). Each stimulus was displayed for 1,500 ms with an inter-stimulus interval of 2,000 ms. Stimuli were presented in two recording sessions, each containing presentation of 120 stimuli in a pseudorandom order with no repeats. Signal Analysis: Analyses were performed by first bandpass filtering raw data of each electrode into broadband gamma activity (BGA; 70-150Hz) following removal of line noise (zero-phase 2nd order Butterworth bandstop filters). A frequency domain bandpass Hilbert transform (paired sigmoid flanks with half-width 1. 5 Hz) was applied and the analytic amplitude was smoothed (Savitzky -Golay finite impulse response, 3rd order, frame length of 201 ms). BGA is presented here as percentage change from baseline level, defined as the period -500 to -100 ms before each word presentation.
This method was selected as currently available cortical surface parcellations have been shown to be inadequate at predicting functional boundaries of task-evoked activity  (Zhi et al. , 2021 ). An exception was made for the STG ROI which was defined using a Human Connectome Project derived parcellation (A1, A4, PBelt, MBelt, LBelt)  (Glasser et al. , 2016). Frequentist statistical methods were corrected for multiple comparisons using a Benjamini-Hochberg false detection rate (FDR) threshold of q < 0. 05. Neural Decoding: Decoding analyses were performed using logistic regression classifiers, using 5fold cross validation, implemented within MNE-Python  (Gramfort, 2013; Gramfort et al. , 2014). For each patient, decoding performance was summarized with an area under the curve (AUC) and a set of classifier weights for each electrode.
The purpose of this work is to evaluate local power and electric field distributions inside high-resolution 3D anatomical CAD models of cutaneous appendages at 60 GHz upcoming for 5G/6G. The microscale resolution models of cutaneous nerves, lymphatics, pilosebaceous unit, microvasculature, eccrine sweat glands, Meissner corpuscle and Pacinian corpuscle were designed based on morphometric data and microscopic images. The Maxwell's and Hanai's mixture equations were employed to retrieve the complex permittivity of cutaneous appendages based on their free water content. The cutaneous appendages were exposed to a uniform plane wave. The finite element method was used to compute the power loss density (PLD) and internal electric field (E). The results indicate that the maximal PLD in cutaneous appendages occurred for E polarized along their longest dimension.
C. NUMERICAL METHOD The full-wave electromagnetic simulations were performed using finite element method implemented in COMSOL Multiphysics (RF module). The source was a TE and TM plane wave (10 W/m 2 at 60 GHz). The periodic boundary conditions (Floquet periodicity) were applied in the x and y directions to simulate an infinite structure. A free space padding of 0. 25 mm was inserted between the periodic port and the skin model (in case of the hair follicle, a padding of 0. 35 mm was added). The outgoing waves were absorbed by 8-layered perfectly matched layer. The computational volume (0. 2 mm × 0. 2 mm × 1. 1 mm) in majority of simulations was discretized into roughly 3 × 10 6 -5 × 10 6 tetrahedral Lagrange quadratic mesh elements. The minimum and maximum mesh cell size was within 0. 05-30 µm range.
This work was supported in part by the  French National Research Program for Environmental and Occupational Health of ANSES  under Grant  2018/2 RF/07  through the  NEAR 5G  Project; in part by the  French Government support  granted to the  CominLabs Excellence Laboratory  and managed by the  National Research Agency in the Investing for the Future Program  under Reference  ANR-10-LABX-07-01 ; in part by the  European Union  through the  European Regional Development Fund (ERDF) ; and in part by the  French Region of Brittany, Ministry of Higher Education and Research ,  Rennes Métropole  and  Conseil Départemental 35 , through the  CPER  Project  SOPHIE/STIC & Ondes.
Pre-trained models have shown very good performances on a number of question answering benchmarks especially when fine-tuned on multiple question answering datasets at once. In this work, we propose an approach for generating a fine-tuning dataset thanks to a rule-based algorithm that generates questions and answers from unannotated sentences. We show that the state-of-the-art model UnifiedQA can greatly benefit from such a system on a multiple-choice benchmark about physics, biology and chemistry it has never been trained on. We further show that improved performances may be obtained by selecting the most challenging distractors (wrong answers), with a dedicated ranker based on a pretrained RoBERTa model.
Introduction In the past years, deep learning models have greatly improved their performances on a large range of question answering tasks, especially using pretrained models such as BERT  (Devlin et al. , 2019) , RoBERTa  (Liu et al. , 2019)  and T5  (Raffel et al. , 2020). More recently, these models have shown even better performances when fine-tuned on multiple question answering datasets at once. Such a model is UnifiedQA  (Khashabi et al. , 2020) , which, starting from a T5 model, is trained on a large number of question answering datasets including multiple choices, yes/no, extractive and abstractive question answering. UnifiedQA is, at the time of writing, state-of-the-art on a large number of question answering datasets including multiple-choice datasets like OpenBookQA  (Mihaylov et al.