dataesr/scientific-paragraphs-categorization huggingface dataset:
Distress Analysis Interview Corpus The Extended Distress Analysis Interview Corpus (E-DAIC)  [14]  is an extended version of WOZ-DAIC  [23]  that contains semi-clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post-traumatic stress disorder. These interviews were collected as part of a large effort to create a computer agent that interviews people and identifies verbal and nonverbal indicators of mental illnesses  [23]. Data collected include audio and video recordings, automatically transcribed text using Google Cloud's speech recognition service, and extensive questionnaire responses. The interviews are conducted by an animated virtual interviewer called Ellie. In the WoZ interviews, the virtual agent is controlled by a human interviewer (wizard) in another room, whereas in the AI interviews, the agent acts in a fully autonomous way using different automated perception and behaviour generation modules.
For the purpose of the Challenge, the E-DAIC dataset was partitioned into training, development, and test sets while preserving the overall speaker diversity -in terms of age, gender distribution, and the eight-item Patient Health Questionnaire (PHQ-8) scoreswithin the partitions. Whereas the training and development sets  include a mix of WoZ and AI scenarios, the test set is solely constituted from the data collected by the autonomous AI. Details regarding the speaker distribution over the partitions are given in Table  2.
Cross-cultural Emotion Database (SEWA) The SEWA database consists of audiovisual recordings of spontaneous behaviour of participants captured using an in-the-wild recording paradigm  [31]. Pairs of friends or relatives from German, Hungarian, and Chinese cultures were recorded through a dedicated video chat platform which utilised participants' ownstandard -web-cameras and microphones. After watching a set of commercials, pairs of participants were given the task of discussing the last advert watched (a video clip advertising a water tap) for up to three minutes. The aim of this discussion was to elicit further reactions and opinions about the advert and the product advertised. The video chats of the three cultures have been annotated w. r. t. the emotional dimensions arousal and valence, and a third dimension describing liking (or sentiment), independently by several native speakers; German and Chinese: six annotators, Hungarian: five annotators.
BASELINE FEATURES Emotion recognition from audiovisual signals usually relies on feature sets whose extraction is based on expertise gained over several decades of research in the domains of speech processing, e. g. , Mel Frequency Cepstral Coefficients (MFCCs), and vision computing, e. g. , Facial Action Units (FAUs). However, recent advances in the field of representation learning, whose objective is to learn representations of data that are best suited for the recognition task  [6] , have shown that efficient representations of audiovisual signals can be learnt in the context of emotion  [2, 59, 71]. Audiovisual representations can be learnt from expert-driven information extracted from the raw signals  [59] , or directly from the raw signals  [71]. They can also be generated using adversarial networks  [13] , or using convolutional neural networks trained on out-of-domain data and for a different task, e.
As visual features, we extract the intensities of 17 FAUs for each video frame, along with a confidence measure, using the toolkit F 3   [4]. Descriptors of pose and gaze are additionally extracted. Audiovisual LLDs are summarised over time by computing their mean and standard-deviation using a sliding window of 4 s length, and a hop size of 1 s for the USoM and E-DAIC datasets, and 100 ms for the SEWA dataset, excepted for the G MAPS set, which is computed on each window.
Bags-of-Words The technique of bags-of-words (BoW), which originates from text processing, represents the distribution of LLDs according to a dictionary learnt from them. As a front-end of the BoW, we use the MFCCs and the G MAPS set for the acoustic data, and the intensities of the FAUs for the video data; MFCCs and G MAPS LLDs are standardised (zero mean, unit variance) in an on-line approach prior to vector quantisation, while this step is not required for the FAU intensities. To generate the BoW representations, both the acoustic and the visual features are processed and summarised over a block of a 4 s length duration, for each step of 100 ms for the SEWA dataset, and 1 s for the USoM and E-DAIC datasets. The codebook size is 100. Instances are sampled at random to build the dictionary, and the logarithm is taken from resulting term frequencies in order to compress their range.
Deep Representations As in last year's challenge  [47] , we have included D S 5 features as a deep learning based audio baseline feature representation  [2]. D S features are inspired by deep representation learning paradigms common in image processing: spectral images of speech instances are fed into pre-trained image recognition CNNs and a set of the resulting activations are extracted as feature vectors. For this year's challenge, we extracted D S features from four robust pre-trained CNNs using VGG 16  [69] , A N  [34] , D N 121, and D N 201  [26] ; A N was used in the AVEC 2019 CES purely for consistency with the previous AVEC 2018 CES. The speech files are first transformed into mel-spectrogram images with 128 mel-frequency bands, a window width of 4 s for all challenge corpora and a hop size of 1 s for the USoM and E-DAIC datasets, and 100 ms for the SEWA dataset.
Following that, the spectral-based images are forwarded through the pre-trained networks. A 4 096-dimensional feature vector is then formed from the activations of the second fully connected layer in VGG 16 and A N , and a 1 024 and a 1 920-dimensional feature vector is obtained from the activations of the last average pooling layer of the D N 121 and D N 201 networks, respectively. We also provide two baseline deep visual representations. For these, we employed a VGG-16  [69]  network and a ResNet-50 network  [24]  that are pre-trained with the Affwild dataset  [30]. The pipeline starts with applying the F toolkit  [4]  to detect the face region and subsequently perform face alignment. Then, we froze the weights of two pre-trained models and fed the aligned face images to both CNNs individually.
Detecting Depression Sub-challenge For the depression detection baseline, we employ a single-layer 64-d GRU as our recurrent network with a dropout regularisation of rate 20 %, followed by a 64-d fully-connected layer to obtain a single-value regression score. To handle bias, we convert the PHQ-8 score labels to floating point numbers by downscaling with a factor of 25 prior to training. The network is trained and evaluated using a CCC loss function and evaluation score, and the RMSE results are reported using the original PHQ scale. A batch size of 15 is used consistently, and the learning rate is optimised across different feature sets. In order for the data to fit on GPU memory, a maximum sequence length has been assigned for the sessions. For the MFCCs and eGeMAPS LLDs, and the high dimensional deep representations like DeepSpectrum, ResNet, and VGG, a maximum sequence length of 20 minutes is used.
Fusion of the different representations achieves the best result on the development set, and the RMSE returned on the test set is slightly better than the one obtained on the DAIC-WoZ dataset with the AVEC 2017 baseline system  [51] ; RMSE = 6. 37 for AVEC 2019 compared to RMSE = 6. 97 for AVEC 2017. However, the baseline system developed for this year's Challenge is more complex -a simple linear regression model vs GRU-RNNs for this year -, and the corresponding scores should be therefore best regarded in the light of the best results of the AVEC 2017 Depression Subchallenge  [22] , which was RMSE = 4. 99. On the basis of the results obtained in the automatic sensing of the level of depression from interactions with the virtual agent, recognition seems more challenging when the agent is solely AI driven, than when a human is driving the agent as a WoZ.
Results indicate that: i) in the SoMS, the level of mood was best predicted when the system was trained on the static scores and evaluated on their dynamic view, i. e. , between the label provided after the storytellings, and before the first story, which can be explained by inertial emotion theories; ii) in the DDS, prediction of the level of depression (PHQ-8) is reported to be more challenging when the virtual agent conducting the interview is wholly driven by AI, compared to a WoZ setup; and iii), in the CES, dimensional emotions are more challenging to sense in a cross-cultural setting for audio descriptors compared to video descriptors, which confirm on one hand the universality of facial expressions for Asian (Chinese) and Western European cultures (German and Hungarian), and show on the other the challenge of using audio descriptors for paralinguistics analysis in languages presenting dissimilarities in their acoustic, in particular when data are collected in an ecological (noisy) environment.
ACKNOWLEDGMENTS The research leading to these results has received funding from the  Horizon 2020 Programme  through the  Research Innovation Action  No. 826506  (sustAGE), and No. 688835  ( DE-ENIGMA). Further funding has also been received from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No. 115902 , which receives support from the  European Union 's  Horizon 2020 research and innovation program  and  EFPIA. The work on the DDS was supported in part by the  U. S. Army. Any opinion, content or information presented does not necessarily reflect the position or the policy of the  United States Government , and no official endorsement should be inferred. The authors further thank the sponsor of the challenge -audEERING GmbH.
It is possible that the observed pattern of increasing species diversity could be explained by better DNA preservation and thus detection of sedaDNA within the uppermost sediments compared to older sediments. Data patterns suggest this is not the case, however. A sustained increase in species diversity is observed until the early to middle Holocene, after which species diversity appears to stabilise (with the exception of four anomalous peaks) towards the most recent samples, with little change that could be attributed to better DNA preservation (Supplementary Fig. S5 ). More than two thirds of all recorded plant taxa identified were already in place in the full-glacial period (ca. 24,000-15,000 cal. years), when the study region remained free of extensive ice cover  42, 52, 53  , and the data provide a rich floristic record of full-glacial vegetation close to local glaciers but outside of the Eurasian Ice Sheet margin, which was situated further to the north  53.
DNA sequence analysis and taxonomic assignments. Next-generation sequence data were filtered using the OBITools software package (Boyer et al. , 2016; http://metabarcoding. org/obitools/doc/index. html) following the protocol and criteria defined by Alsos et al. 35. Taxonomic assignments were performed by first matching sequences against a reference library of 2445 sequences comprising 815 artic  32  and 835 boreal vascular plants  29  , in addition to 455 arctic/boreal bryophytes  33. Sequences were then matched to a second reference library generated after running ecopcr on the global EMBL database (release r117). In order to minimise any erroneous taxonomic assignments, only taxa with a 98% match, or greater, to a reference sequence were retained. A minimum threshold of at least 10 reads per PCR replicate was used, except in the case of the arctic-alpine plant taxa (see Fig.
2 ) where <10 reads per PCR replicate occurrences are highlighted. We further removed sequences that displayed higher average frequency in the negative subsampling (water), extraction or PCR controls than in the lake sediment samples in which they were present. Identified taxa were compared to the circumpolar flora  81  , Pan Arctic Flora Checklist 82 and flora of the Polar Urals  [83] [84] [85]. Sequences with <100% match to a reference sequence and/or those assigned to taxa that are not present in northern Russia today were carefully crosschecked against the NCBI BLAST database for multiple or alternative taxonomic assignments (http://www. ncbi. nlm. nih. gov/blast/). Taxonomy follows the Pan Arctic Flora Checklist 82 , if present.
Full details of the classifications, including likely species of which the classification is based upon, are presented in Supplementary Table  S2. Bryophytes were excluded from the analysis due to the lack of sufficient information on their present-day distribution. To determine the degree of species persistence over time and long-term continuity of individual plant taxa identified by sedaDNA, an assessment of their present-day occurrence within the local vegetation mosaic of Lake Bolshoye Shchuchye's hydrologic catchment was performed based on data collected by L. Morozova and colleagues during the years 2000, 2001 and 2006 for a period of 46 days in total. Botanical data were collected systematically along topographical/ecological transects from rivers/lakes up to the top of a ridge or mountain.
Acknowledgements This study was jointly supported by the  Research Council of Norway  through the multinational research projects " Climate History along the Arctic Seaboard of Eurasia (CHASE) " (grant. no. NRC 255415  to  J. I. Svendsen ), " AfterIce " (grant no. 213692/ F20  and  230617/ E10  to  I. G. Alsos ), " ECOGEN " (grant no. 250963/F20  to  I. G. Alsos ) and a PhD studentship for C. L. Clarke provided by the  UK Natural Environment Research Council  (grant no. NE/ L002531/1 ). We thank  Marie Kristine Føreid Merkel  and  Anita-Elin Fedøy  for assistance during laboratory work,  Frédéric Boyer  for raw sequence data handling and  Eva Bjøseth  for cartographic help in producing the location map of Lake Bolshoye Shchuchye. We also thank  Francesco Ficetola  and one anonymous reviewer for their time and constructive feedback on the manuscript.
, 2016) , maintaining biological activity for Wnt3 and Wnt3a and partial activity for Wnt1, Wnt7a, Wnt7b, Wnt8a, Wnt9b and Wnt10b, which play a crucial role in cell differentiation during embryogenesis and are involved in the development of various diseases, including cancer  (Nusse & Varmus, 2012; Nile & Hannoush, 2019). A compelling computational model of lipid-bound afamin in a monoclinic crystal form (PDB entry 5okl) docked with a homology model of Wnt3a indicated that afamin can capture the acyl chain of the palmitoylated Ser209 of Wnt3a in a deep hydrophobic pocket  (Naschberger et al. , 2017). The preparation and crystallization of afamin has posed significant challenges  (Altamirano et al. , 2018). In contrast to human serum albumin (hSA), which is exported from the liver as a nonglycosylated chain, human afamin is highly and variably enzymatically glycosylated in vivo  (Jerkovic et al.
, 2018)  and the R free reflections from dehydrated orthorhombic afamin (PDB entry 6fak) were used for all orthorhombic data sets. Despite poor merging statistics and low completeness (Table  1 ), the anisotropycorrected data gave clear molecular-replacement solutions in space group No. 18, P2 1 2 1 2, but with PDB entry 5okl chain A from monoclinic afamin as a template  (Naschberger et al. , 2017 ) using Phaser (McCoy et al. , 2007). Although no significant anomalous signal could be extracted from the data recorded at 12. 835 keV (above the Gd L III edge of 7. 243 keV), strong positive difference peaks indicated the likely presence of three Gd sites, of which two were in a pair exhibiting the same Gd-Gd distance as observed in high-resolution models of lysozyme complexed with Gd-DO3A  (Gorel et al.
Contextual flexibility affects binding-site analysis One of the most interesting insights gained from the comparison of the four different afamin structure models, the monoclinic model MA (PDB entry 5okl chain A), the monoclinic model MB (PDB entry 5okl chain B), the dehydrated orthorhombic model OA (PDB entry 6fak chain A) and the orthorhombic Gd-DO3A complex GD (see Section 3. 4), is that the relative motions of the domains forming the deep cleft in the centre of the heart-shaped molecule (following the hSA description) do affect the shape of the deep hydrophobic binding pocket at the equivalent of the Sudlow 1 (S1) drugbinding site in hSA  (Naschberger et al. , 2017). The breathing motions that occur in the transition from the more open MB conformation to the tighter MA and OA conformations (Fig.
The log-likelihood gain and final TFZ scores were identical within 5% regardless of which anisotropy correction was applied, but were distinctly lower without any anisotropy correction. The success of MR even in the absence of anisotropy correction is not surprising because the molecular envelope is largely determined by lowresolution reflections, which were less affected by anisotropy and completeness. The best solution was obtained with the monoclinic afamin search model MA (PDB entry 5okl chain A, TFZ 27. 9, R free 0. 48), while the worst solution was obtained with the dehydrated orthorhombic afamin model OA (PDB entry 6fak chain A, TFZ 22. 5, R free 0. 52). The reason might be that the domain arrangements of hSA domains I and III (Fig. Gd-DO3A sites in lysozyme and afamin. The top panel shows the arrangement of the Gd-DO3A sites in a high-resolution model of lysozyme (PDB entry 1h87;  Girard et al.
Refinement of the model stalled at R free values of $0. 36 despite good geometry for the traceable part of the model. At this point, Gd atoms were placed with occupancy set to 0. 7 so that the B factors refined to values of about 150% of the mean environmental B factor. Subsequent occupancy refinement indicated Gd occupancies of between 0. 9 and 0. 7. The protrusions in the large density blobs and difference density suggested additional features around the Gd atom, but we were unable to determine and refine the orientation of the DO3A 'crown' around the Gd-DO3A. However, the refined distance of 6. 1 A ˚between the two Gd peaks corresponds exactly to the distance observed in the high-resolution lysozyme structures, and we placed, but did not refine, the DO3A crowns in the two poses determined in PDB entry 1h87  (Girard et al.
1 A ˚, as in the lysozyme model, with sufficient space in the binding site to accommodate the DO3A crowns of the Gd-DO3A complexes. The bottom centre panel shows mF o À DF c positive OMIT difference electron density displayed at a 2. 5 level in green and 2mF o À DF c density displayed at 0. 8 in blue. The DO3A crowns are not refined but are placed in an orientation corresponding to that in PDB entry 1h87. The methyl groups of the placed DO3A crowns face towards a phenylalanine lining the bottom (left side of the figure) of the binding pocket, resembling the interactions observed in PDB entry 1h87. A similar interaction can be proposed for the third Gd site located at a crystal contact in afamin (bottom right). The top figure was produced with ICM-Pro from Molsoft (Abagyan et al. , 2006) and the electron-density figures were produced with Coot (Emsley et al.
Methods study population. Data are drawn from the Whitehall II cohort study that was established in 1985/88 on 10308 British civil servants (67% men) aged 35-55 years  28. The study design consists of a clinical examination every 4-5 years since inclusion. Accelerometer measurement was added to the study at the 2012/13 wave of data collection (age range = 60-83 years) for participants seen at the central London clinic and for those living in the South-Eastern regions of England who had their clinical assessment at home. Participants gave informed, written consent to participate, and the University College London Hospital Committee on the Ethics of Human Research approved the study, reference number 85/0938. All experiments were performed in accordance with relevant guidelines and regulations.
physical activity. During the 2012/13 clinical examination, participants were asked to wear a validated  29  triaxial accelerometer (GENEActiv; Activinsights Ltd, Kimbolton, Cambs, UK) on their non-dominant wrist for 9 consecutive, 24-hour, days. The data processing has been described elsewhere  23. In brief, the accelerometer sampled data at 85. 7 Hz rate, acceleration was expressed relative to gravity (g units; 1 g = 9. 81 m. s -2 )  30  , averaged over 5-second epochs  [31] [32] [33]  , and corrected for calibration error  34. Accelerometer data were processed in R using the GGIR package version 1. 2-11 (https://cran. r-project. org/ src/contrib/Archive/GGIR/). Sleep periods were detected using a validated algorithm aided by a sleep log  35. Data from the first waking up (day 2) to waking up on the day before last day (day 8) were used, corresponding to 7 full days.
Only waking periods were retained in the analysis, that is periods between waking and sleep onset (as opposed to the night period). Participants were included in the analysis if they had valid data, defined as daily wear time ≥2/3 of waking hours, for at least 2 weekdays and 2 week-end days. In those with valid data, nonwear time was corrected for using a previously reported algorithm  30, 33, 36. In order for the activity undertaken to be classified as MVPA, mean acceleration over 5s-epoch needed to be ≥0. 10g, between 0. 03g and 0. 10g for light activity, and <0. 03g for sedentary behavior  21, 23, 32, 37. The daily time in different activity level was calculated as the mean of measures over 7 days. For participants with <7 valid days (N = 117), data from weekend and week-days were weighted to represent a 7-day week  [21] [22] [23].
Then, to assess whether the association differs by smoking history, we included interaction terms between time spent in activity level under consideration and smoking history (see equation in Supplementary Methods 2). No interactions were found between other covariates and smoking history (all p for interaction >0. 22) leading us to not include interactions terms with other covariates in the model. In order to assess whether the associations observed were driven by presence of respiratory diseases, analyses were repeated excluding participants with respiratory disease. Association between accelerometry distribution and expired air volume curve using functional data analysis. For each participant indexed by i, the distribution density function of the 5s-epoch acceleration intensities of the waking periods during the observation, designed by f i , was determined using a kernel density estimation  39  with a Gaussian kernel and a plug-in bandwidth selector method  40  implemented in the package "ks" of the R software (version 3.
Acknowledgements The  Whitehall II  study has been supported by grants from the  UK Medical Research Council  ( K013351 ,  R024227 ,  S011676 ); the  British Heart Foundation  ( PG/11/63/29011  and  RG/13/2/30098 ); the  British Health and Safety Executive ; the  British Department of Health ; the  National Heart, Lung, and Blood Institute  ( R01HL036310 ); the  National Institute on Aging, National Institute of Health  ( R01AG056477 ,  R01AG034454 ); the  Economic and Social Research Council  ( ES/J023299/1 ). Mika Kivimaki  is supported by the  Medical Research Council  ( K013351 ,  R024227 ,  S011676 ),  UK ,  NordForsk , the  Nordic Programme on Health and Welfare , and the  Academy of Finland  ( 311492 ).