dataesr/scientific-paragraphs-categorization huggingface dataset:
S3B ), whereas for 'both sexes' and for males, the more pronounced, typical jump across the F2-F3 point occurs. When we applied machine learning techniques to all participants, for discrimination between less and more severe NAFLD, classification was best across the aforementioned F2-F3 transition. Here, and to our surprise, sex was the most unimportant feature when classifying fibrosis, in all models constructed, where it ranked at the lowest position of importance. This was true even when using only clinical variables to distinguish F0-F2 vs. F3-F4 (see Table  S4 ). Across the F2-F3 transition, using the whole dataset (male and female), we observed increases in several PCs and TGs and decreases in PC(O)s, LPCs, and SMs, as well as several metabolites including 2-and 3-HB. Increased 2-HB was previously found as an early indicator of insulin resistance  30  and worsening glucose tolerance.
Financial support This study received support from the  Elucidating Pathways of Steatohepatitis (EPoS)  consortium funded by the  Horizon 2020 Framework Program  of the  European Union  under Grant Agreement 634413; the  Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS)  consortium funded by the  Innovative Medicines Initiative  ( IMI2) Program  of the  European Union  under Grant Agreement 777377; this Joint Undertaking received support from the  European Union 's  Horizon 2020 Research and Innovation Programme and EFPIA ; and the  Newcastle NIHR Biomedical Research Centre  and the  European NAFLD Registry.
Analysis of Cell Cycle and Meiotic Progression For DNA content analysis by flow cytometry, cells were fixed in 70% cold ethanol, washed in 50 mM sodium citrate and treated with RNase A (0. 1 mg/mL) at 37 • C overnight. Samples were then stained using 2 mg/mL propidium iodide, sonicated (Branson Digital Sonifier; Branson Ultrasonics, Brookfield, CT, USA), and analyzed using a BD Accuri C6 flow cytometer (Becton Dickinson, Franklin Lakes, NJ, USA). Analysis was performed using the FlowJo analysis software. Note that the fission yeast cell cycle differs from that of a number of model systems, as the G1 phase is short and S phase occurs prior to cytokinesis. As a result, haploid cells have a 2C DNA content during most phases of their cell cycle. The appearance of a 1C peak in nitrogen-starved cells is the result of cell cycle exit and arrest in a quiescent state in these conditions: Cytokinesis occurs without an ensuing S phase.
To assess meiotic progression, cells were ethanol fixed and stained with DAPI (2 mg/mL) to visualize nuclei. Samples were counted every 15 min to determine the kinetics of meiosis I and meiosis II: cells harboring two nuclei indicate completion of meiosis I, and four nuclei are observed upon completion of meiosis II. Microscopy was performed using an inverted Zeiss Axio Observer (Carl Zeiss Microscopy GmbH, Jena, Germany) equipped with a Lumencor Spectra X illumination system and an Orca Flash 4. 0V2 sCMOS camera (Hamamatsu Photonics, Hamamatsu City, Japan). Images were acquired using the VisiView software (Visitron Systems GmbH, Puchheim, Germany) and a Plan-Apochromat 63X/1. 4 NA immersion lens (Carl Zeiss Microscopy GmbH, Jena, Germany).
non-replicated DNA represents 100% replication, so all values >2 were removed. Next, for each experiment, the geometric means over five consecutive probes were determined across the genome for each reciprocally labelled dataset, and these datasets were averaged. Two biological repeats were performed for each strain, and we took the average of the data from these two experiments. For analysis and comparison of copy number, the baselines between different conditions, representing unreplicated DNA, were matched and set to 1 using a previously described approach  [50]. Briefly, as cells are in HU, there are genomic regions that remain unreplicated in our S phase samples, and these will have the lowest values in each dataset. Therefore, for each averaged set of biological repeats, we took the lowest 10% of the ratios of replicated vs.
unreplicated DNA and calculated their median, then normalized the dataset to this value. The following correction factors were applied: Control I-0. 04; Inversion I-0. 04; Control II-0. 04; Inversion II-0. 046; Pat1 Control I-0. 048; Pat1 Inversion I-0. 046; Pat1 Control II: 0. 044; Pat1 Inversion II-0. 037. Unless otherwise stated, the replication profiles shown in the figures are these averaged and baseline-corrected datasets, with copy numbers converted into percent efficiency (e. g. , a ratio of 1. 5 corresponds to 50% efficiency). Note that a consistent low outlier value at chII: 2. 9838 × 10 6 was removed from the profiles of Control cI, Inversion cI, Inversion cII, and Pat1 Control cII. To identify origins and determine their efficiencies, we applied LOESS regression (5000 bp 2nd radius) to the averaged corrected dataset for each strain.
Sites with ratios greater than 1. 05 in either of the Inversion backgrounds were then added. To establish a similar list for pre-meiotic S phase (276 total sites), the same procedure was applied using the PatI Control and Pat1 Inversion datasets. Copy numbers were then converted into efficiency. These sets of origins were used to generate the profiles of replication domains in Figures  2B  and 5B. To identify origins that showed changes in usage due to the engineered chromosomal inversions, we began by establishing the level of reproducibility between the experiments we performed for the control strains. The Pearson's correlation coefficient (r) for pairwise comparisons of origin efficiencies in Control cI vs. cII and Pat1 Control cI vs. cII showed strong positive correlations (r = 0. 9884 and r = 0.
9865, respectively; p < 0. 00001 in both cases). In addition, we estimated the experimental noise in our origin mapping assays, determining the differences in origin efficiencies between the pairs of control strains. The median and median absolute deviation (MAD) of these differences show that the compared datasets are virtually identical (Control cI vs. cII: 1. 6% ± 1. 1%; Pat1 Control cI vs. cII: -1. 5% ± 1. 2%). Thus, sites with altered replication initiation due to the chromosomal inversions correspond to those whose differences in efficiency between each Inversion/Control pair are significantly beyond the variability of the method, applying a stringent cutoff of Q3 + 3IQR and Q1 -3IQR. To validate the analyses above, we further demonstrated the reproducibility of our methodology for determining origin efficiencies.
Shi  31  estimated China CEs distribution base on NTL at province level with a spatial resolution of 1 km. However, the map of CEs lacks the details of sectors. Traditional method of mapping CEs based on NTL have the disadvantages of low spatial resolution, larger than 1 km, and inability to distinguish emission sectors. Moreover, Cai  35  developed the China high-resolution emission database (CHRED), which was constructed using the bottom-up method and a spatial resolution of 1 km, combined socio-economic data. Most of the existing spatial CEs studies were mapped based on the NTL at a spatial resolution of 1 km. Thus, NTL-based CEs distribution still has limitations. On one hand, the distribution of CEs does not reflect further details of its spatial heterogeneity at the city level. Low spatial resolution cannot satisfy the application of fine urban carbon management and emission reduction strategy development.
Materials. To estimate the CEs distribution in Shanghai, geospatial and statistical data were used. Geospatial data included POI and land use data. The POI data for 2000, 2005, 2010, and 2015 were in vector format. This was obtained from the Gaode map (https:// ditu. amap. com/) using Application Programming Interface (API) technology. We used 2002 POI data replaced the year of 2000, because Gaode was founded in 2001 and provided services in 2002. The POI data includes the residential, industrial, and tertiary sectors points. Residence community (RC) POI and industrial enterprise (IE) POI contain residential location information and type of enterprises, respectively. The residential area was estimated using statistical data from the Shanghai Statistical Yearbook (SSY) and the website of residential rental companies (e.
g. , https:// sh. lianj ia. com/) by WCT. The land use data were in raster format with a spatial resolution of 30 m, they were obtained from the Resource and Environment Data Cloud Platform (REDCP, http:// www. resdc. cn/ Achie vemen tList. aspx). The land use data included cropland, forest, grass, water, construction land, and unutilized land. Statistical data included the energy balance tables (EBTs) of Shanghai, population (POP), GDP, resident consumption, and energy consumption of key energy-consuming industrial enterprises (ECKEIE, Table  1 ) of Shanghai. These data were obtained from SSY and are available online (http:// www. cnki. net/). The EBT contains data on more than 15 types of energy consumption from industrial, tertiary, and residential sectors.
Acknowledgements The work was supported by the  Shanghai Committee of Science and Technology Fund  ( 22ZR1419300 ,  19DZ1203303 );  National Key R&D Program of China  ( 2017YFC0505703 );  Science and Technology Project of Education Department of Jiangxi Province  ( GJJ200412 ) and the  National Natural Science Foundation of China  ( 41801314 ). Cheng Huang acknowledge the financial support from  China Scholarship Council  ( 201706140147 ) and logistics support from  Purdue University.
HONO Spectral Ratio and Retrieval Method We use the spectral window between 785 and 795 cm -1 for HONO detection and retrievals (Figure  1 ). The spectroscopic parameters for HONO originate from a preliminary version of the linelist for the ν 4 bands of the HONO Trans-and Cis-conformers that is now available in the GEISA database  [14, 31]. The selected 785-795 cm -1 microwindow corresponds to the HONO trans-ν 4 band, where the line intensities are about 12% larger than in  [14]. This is largely within the estimated accuracy associated to this entity (about 30%). Carbon dioxide and water vapor are the two main interferers in this window. Their spectroscopic data are taken from HITRAN 2004  [32]. instruments flew, the first one since October 2011 and the second one since November 2017. In this study, we used the first of these.
HONO Spectral Ratio and Retrieval Method We use the spectral window between 785 and 795 cm -1 for HONO detection and retrievals (Figure  1 ). The spectroscopic parameters for HONO originate from a preliminary version of the linelist for the ν4 bands of the HONO Trans-and Cis-conformers that is now available in the GEISA database  [14, 31]. The selected 785-795 cm -1 microwindow corresponds to the HONO trans-ν4 band, where the line intensities are about 12% larger than in  [14]. This is largely within the estimated accuracy associated to this entity (about 30%). Carbon dioxide and water vapor are the two main interferers in this window. Their spectroscopic data are taken from HITRAN 2004  [32]. HONO is a weak absorber and has been detected by IR satellite instruments only in fire plumes  [12] [13] [14].
Pixels are considered within the HONO plume when their spectral ratio is smaller than the mean of the distribution minus 3σ for the corresponding radiance. cording to the radiance values (Figure  2 ) to delimit spatially the plumes. Pixels are considered within the HONO plume when their spectral ratio is smaller than the mean of the distribution minus 3σ for the corresponding radiance. The retrieval is performed for the pixels identified as within the plume. For these pixels, the radiative transfer (RT) is modeled using the KOPRA RT model [33] and the retrieval is performed by its inversion module, KOPRAFIT. The general framework of the retrieval is developed in  [34]. The measured radiances y can be expressed as: 𝒚 = 𝐹( 𝒙 ) + 𝜺 (1) where x is the atmospheric state, F the radiative transfer model, and ε the measurement noise.
Forward Trajectories Simulations The HYSPLIT model constrained by the Global Data Assimilation System (GDAS) is used to compute forward trajectories simulations. We use the matrix calculation option of HYSPLIT. A 3 × 3 matrix centered at the fire detection location (36. 5° S, 149° E) and time (4 UTC on 4 January) is used. The spatial resolution of the matrix at the starting point is 0. 5°. Fourteen trajectory simulations have been done over 36 h, each of them being initialized every kilometer from 1 to 14 km. Using all the simulated trajectories, we calculate the horizontal density map of the trajectories. For that, the domain of study is gridded at a 1 The retrieval is performed for the pixels identified as within the plume. For these pixels, the radiative transfer (RT) is modeled using the KOPRA RT model [33] and the retrieval is performed by its inversion module, KOPRAFIT.
Model simulations, aiming at reproducing partly this exceptional event, confirm that the plume is transported in the free troposphere. It is more difficult to identify which processes explain the observed HONO vmrs due to large uncertainties in both simulations and observations. In particular, the question of the processes involved to explain the persistent HONO concentrations at sunrise is not resolved at that stage, the model showing a much stronger decrease of HONO when the sun rises, which points to remaining unknowns in chemistry and aerosol loading in the plume. the French Data and Service for the Atmosphere (AERIS) portal via https://eccad. aeris-data. fr/ (Last access date: 12 September 2022) with registration. Version 5. 1. 0 of the HYSPLIT transport and dispersion model used to calculate trajectories of fire plumes in this study is preserved at https://www.
Acknowledgments: The authors acknowledge the different data centers to make satellite and emission data available: the Data and  Service for the Atmosphere (AERIS)  portal and the  NASA GESDISC  system. The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT model and/or READY website used in this publication. We acknowledge the  Institut für Meteorologie und Klimaforschung (IMK), Karlsruhe, Germany , for a licence to use the KOPRA radiative transfer model. This work was granted access to the HPC resources of TGCC under the allocations  A0070107232  and  A0090107232. The IASI mission is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France).
Data Availability Statement: The IASI Level 1C data used for the HONO observations in fire plumes in the study are available at the French Data and Service for the Atmosphere (AERIS) portal via https://iasi. aeris-data. fr/ (Last access date: 12 September 2022) with registration. The CrIS Level 1 data used for the HONO observations in fire plumes in the study are available at the GES DISC NASA portal via https://sounder. gesdisc. eosdis. nasa. gov/data/SNPP_Sounder_Level1 /SNPPCrISL1BNSR. 2/ (Last access date: 12 September 2022) with registration. The AIRS CO Level 3 data are available also at the GES DISC NASA portal (https://disc. gsfc. nasa. gov/ (Last access date: 12 September 2022). V2020r3 of the CHIMERE model used for simulating HONO in Australian fire is available via the GNU General Public License with registration and developed openly at https://www.
These mathematical models have not combined heterogeneous elements nor quantified how much the early SIR/SEIR predictions might be biased. In this paper, we include geographical distributions based on an entire population, using population data of Denmark. When the SIR model was originally formulated, 95 years ago, data was not available to investigate the effects of geographical and demographic differences among the population, which might be one of the reasons why fundamental properties for diseases, such as the basic reproduction number (R 0 ), can vary significantly between different regions  [21]. However, with modern collection of data, these geographical aspects might be accounted for. Our main goal of this work is therefore to investigate the importance of heterogeneities in a geographically distributed population on the spread of a pandemic.
Acknowledgements. The authors are grateful to the Danish expert group of SARS-CoV-2 modelling led by  Statens Serum Institute , especially  Robert L. Skov ,  Kåre Mølbak ,  Camilla Holten Møller ,  Viggo Andreasen ,  Kaare Graesbøl ,  Theis Lange ,  Carsten Kirkeby ,  Frederik P. Lyngse ,  Matt Denwood ,  Jonas Juul ,  Sune Lehman ,  Uffe Thygesen  and  Laust Hvas Mortensen. Furthermore, we thank  Kim Sneppen  for valuable discussions.
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under covariate shift and inefficient for knowledge transfer. In this work, we propose to address these challenges from a causal representation perspective. We first introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables, namely invariant mechanisms, style confounders, and spurious features. We then introduce a learning framework that treats each group separately: (i) unlike the common practice of merging datasets collected from different locations, we exploit their subtle distinctions by means of an invariance loss encouraging the model to suppress spurious correlations; (ii) we devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph; (iii) we introduce a style consistency loss that not only enforces the structure of style representations but also serves as a selfsupervisory signal for test-time refinement on the fly.
This causal formalism motivates us to treat each group distinctively with the following three components. First, we propose to promote causal invariance of the learned motion representations by seeking the commonalities across multiple domains. Oftentimes, the training dataset is not collected from a single place but comprises multiple subsets from different locations. Previous work typically merges them into a larger one, e. g. , the notable ETH-UCY datasets  [39, 55]. However, each subset is often inherently different  [13]. Directly combining them not only entails a risk of biases but also destroys the critical information about the stability of correlations. To address this issue, we train the forecasting model with a penalty on the variation of the empirical risk across various environments.
We validate the proposed method in two settings: synthetic simulation datasets and controlled real-world experiments. In the presence of spurious correlations, the motion forecasting models trained by our invariant loss demonstrate superior out-of-distribution (OOD) generalization ability over previous methods. Under variations of motion styles, our proposed modular architecture and style loss greatly improve the transferability of the model in the low-shot setting. We hope our findings will pave the way for a tight integration of causal modeling and representation learning for motion forecasting, a largely under-explored yet highly promising direction towards reliable and adaptive autonomy.
Related Work Motion Forecasting. Modern motion forecasting models [2,  28, 34, 37, 62 ] are largely built with neural networks and trained with the maximum likelihood principle. Despite strong performance for short-range predictions within the training domain, they often struggle to generalize under covariate shift. Recently, a couple of works proposed to promote their robustness using negative data augmentation  [43, 87]. However, designing negative examples of highdimension, e. g. , long sequences, can be difficult in practice. Our work explores a causality-inspired alternative that does not require hand-engineered interventions over training data and is hence more theoretically grounded and algorithmically generic. Closely related to ours, another recent work [13] attempts to mitigate biases in motion datasets through counterfactual analysis.
Our method differs from theirs in three aspects: (i) their approach learns to estimate dataset biases before subtracting them in the feature space, whereas our method aims to directly suppress biased features; (ii) their approach inherits the merge-and-shuffle convention, which destroys some critical information for bias estimation; in contrast, we keep each subset separately and exploit unstable correlations across environments; (iii) the counterfactual problem formulated in their approach is generally difficult to solve (cf. Pearl's ladder of causation [54]); conversely, we consider spurious correlations from the interventional perspective, which is easier to tackle in practice. Causal Learning. The intersection of causal inference and machine learning has been a vibrant area of research in the past few years  [20, 57, 78].
Formalism of Motion Forecasting Preliminary. Consider a motion forecasting problem in multi-agent environments. For a scene of M agents, let s t = {s 1 t , • • • , s M t } denote their joint state and s i t = (x i t , y i t ) denote the state of an individual agent i at time t. The model takes an input sequence of past observations x = (s 1 , • • • , s t ) in order to predict their states in the future y = (s t+1 , • • • , s T ) up to time T. Modern forecasting models are largely built with encoder-decoder neural networks, where the encoder φ(•) extracts a compact motion representation z of the past observations and the decoder g(•) rolls out the predicted trajectory ŷ. The training data D is often collected from a set of K environments E = {e 1 , e 2 ,. , e K }. Previous work typically merges them into a large dataset and assumes the mixture as a representative of the unseen test environment ẽ.
Setup The original ETH-UCY dataset contains five subsets collected at different locations  [39, 55]. While recent work  [13]  has highlighted the intrinsic differences between these subsets, it is still not trivial to pinpoint the detailed biases in each environment. To clearly examine the robustness of a motion forecasting model against non-causal biases, we modified the ETH-UCY dataset by introducing a third input variable measuring the level of observation noises, given that variations of spurious noise often occur in real-world problems  [4]. Specifically, for each agent at time step t, we assign the observational uncertainty σ t based on a linear function of the local trajectory curvature: c t := ( ẋt+δt -ẋt ) 2 + ( ẏt+δt -ẏt ) 2 , σ t := α • (c t + 1), (6) where ẋt = x t+1 -x t and ẏt = y t+1 -y t reflect the velocity of the agent within the temporal window of length δt = 8, and α is a domain specific parameter to control the strength of spurious features.
It is also visually distinct that a large emphasis (λ in Eq. 3) on the invariant penalty term during training leads to a more robust model under spurious shifts. In Figure  5  we visualize the qualitative results on a particular test example of the augmented ETH-UCY dataset. While the input trajectories remain the same across all domains, a growing strength of the spurious feature causes a dramatic shrinkage of the predicted trajectories from the baseline methods. In contrast, the outputs of our method stay almost constant under spurious shifts.