dataesr/scientific-paragraphs-categorization huggingface dataset:
By default, non-neuronal confounds were removed from the timeseries, but to allow an analysis of the effect of TUS on non-neuronal signal the data was also processed in parallel in a second pipeline, distinct from the default by omitting the non-neuronal confound regression procedure. Bottom right panel: The effect of TUS on the coupling patterns of the stimulated regions were quantified as 'connectional fingerprints' employing pre-defined targets (here illustrated for the SMA in the control state). These fingerprint analyses allowed us to test for the presence and duration of TUS effects in the stimulated areas (SMA after SMA TUS; FPC after FPC TUS), and in control areas, including non-stimulated areas (FPC after SMA TUS; SMA after FPC TUS) and the auditory cortex. Whole brain connectivity maps supported exploratory analyses and an assessment of the impact of TUS on non-neuronal signal (here illustrated for SMA in the control state).
This suggests an additive non-neuronal source, captured by signal components in the meningeal compartment, may explain the presence and enhancement of global signal observed following TUS over SMA (Figure  6e ,f, e. g. primary sensorimotor cortex). These effects of TUS on widespread coupling mediated by meningeal signal persisted over time for more than 1 hr after stimulation had ended (Figure  6g ). Similar effects of TUS on meningeal signal were also observed after FPC TUS (Figure  6 -figure supplement 1). We note that in unconfounded fMRI data FPC TUS did not have a strong impact outside the stimulated region (illustrated for POp in Figure  6 -figure supplement 1). In contrast, in fMRI data confounded by meningeal-driven global nuisance, FPC stimulation led to widespread enhanced signal coupling compared to the control state.
Stimulation was a 40 s train that ended at least 20 min before the fMRI data acquisition period. The sustained nature of the train and other features of the stimulation pulses may make the protocol used here more effective for neuromodulation, while ensuring the thermal modulation of the cortex remains limited (<1 ˚C, Figure  2 ). Such limited thermal changes are not associ- ated with neuromodulatory effects observed more than 30 min after the stimulation: the thermal rise is short-lived  (Dallapiazza et al. , 2018) , not accompanied by tissue damage and below the thermal effects observed with some protocols in rodents (for a review, see  Constans et al. , 2018). In experiment 3 we found that TUS had reproducible effects. When TUS was applied to FPC in three other individuals, it induced spatially specific effects similar to those seen in experiment 2.
Acoustic and thermal modelling The acoustic wave propagation of our focused ultrasound protocol (at 130 V peak-to-peak voltage) was simulated using a k-space pseudospectral method-based solver, k-Wave  (Cox et al. , 2007)  to obtain estimates for the pressure amplitude, peak intensity, spatial distribution, and thermal impact at steady state. 3D maps of the skull were extracted from a monkey CT scan (Kyoto University online database, ID 1478, 0. 26 mm isotropic resolution). Soft tissues were assumed to be homogeneous, with acoustic values of water ( tissue =1000 kg/m 3 and c tissue =1500 m/s). In the bone, a linear relationship between the Hounsfield Units (HU) from the CT scan and the sound speed, as well as the density, was used. The power law model for attenuation is a att ¼ a 1 Ã f b where the porosity f is defined by f ¼ maxÀ maxÀwater in the skull  (Aubry et al.
, 2012). The last term corresponds to the perfusion process: w, b , C b , and T a correspond to the blood perfusion rate, blood density, blood specific heat and blood ambient temperature respectively. These parameters are assumed homogeneous over the brain, although a more detailed description of the brain cooling processes can be found in the literature  (Wang et al. , 2015). The perfusion parameters are based on previous reports  (Pulkkinen et al. , 2011) : w=0. 008s À1 ; b = 1030 kg. m À3 ; C b = 3620 J. kg À1. K À1 and T a = 37 ˚C. The bioheat equation is solved by using a 3D finite-difference scheme in MATLAB (Mathworks, Natick, USA) with Dirichlet boundary conditions. Initial temperature conditions were 37 ˚C in the brain, skull and tissue, and 24 ˚C in the water coupling cone. Simulations were run over 1 min presonication, followed by 40 s of sonication, and 5 min post-sonication, closely following the experimental procedure.
Macaque MRI acquisition We acquired one MRI session per monkey per condition: in total we performed three sessions per animal across experiments 1 and 2, and one session per animal in experiment 3. The ultrasound sonication and subsequent MRI scans were performed under inhalational isoflurane gas anaesthesia using a protocol which has previously proven successful in preserving whole-brain functional connectivity as measured with BOLD signal  (Mars et al. , 2013; Mars et al. , 2011; Neubert et al. , 2015; Neubert et al. , 2014; O'Reilly et al. , 2013; Sallet et al. , 2013; Vincent et al. , 2007). In the case of the TUS conditions, fMRI data collection began only after completion of the TUS train. Anaesthesia was induced using intramuscular injection of ketamine (10 mg/kg), xylazine (0. 125-0.
During the acquisition of the functional data the expired isoflurane concentration was in the range 0. 6-0. 8%. Isoflurane was selected for the scans as it was previously demonstrated to preserve rs-fMRI networks  (Mars et al. , 2013; Mars et al. , 2011; Neubert et al. , 2015; Neubert et al. , 2014; O'Reilly et al. , 2013; Sallet et al. , 2013; Vincent et al. , 2007). Macaques were maintained with intermittent positive pressure ventilation to ensure a constant respiration rate during the functional scan, and respiration rate, inspired and expired CO 2 , and inspired and expired isoflurane concentration were monitored and recorded using VitalMonitor software (Vetronic Services Ltd. ). Core temperature and SpO 2 were also constantly monitored throughout the scan. A four-channel phasedarray coil was used for data acquisition (Dr.
H. Kolster, Windmiller Kolster Scientific, Fresno, CA, USA). FMRI data were collected in experiments 1-3. In each session whole-brain BOLD fMRI data were collectedfor 3 runs of approximately 26 min each, using the following parameters: 36 axial slices; inplane resolution, 2 Â 2 mm; slice thickness, 2 mm; no slice gap; TR, 2000 ms; TE, 19 ms; 800 volumes per run. A minimum period of 10 days elapsed between sessions. T1-weighted structural MRI scans were collected in experiments 1-3. A structural scan (average over up to three T1w images acquired in the same session) was acquired for each macaque in the same session, using a T1 weighted magnetization-prepared rapid-acquisition gradient echo sequence (0. 5 Â 0. 5Â0. 5 mm voxel resolution).
Macaque anatomical MRI pre-processing The pre-processing and analysis of the MRI data was designed to follow the HCP Minimal Processing Pipeline  (Glasser et al. , 2013) , using tools of FSL (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki; RRID:SCR_ 002823), HCP Workbench (https://www. humanconnectome. org/software/connectome-workbench; RRID:SCR_008750), and the Magnetic Resonance Comparative Anatomy Toolbox (MrCat; https:// github. com/neuroecology/MrCat; copy archived at https://github. com/elifesciences-publications/ MrCat;  Verhagen, 2019). The T1w images were processed in an iterative fashion cycling through brain-extraction, RF bias-field correction, and linear and non-linear template registration to the Macaca mulatta F99 atlas  (Van Essen and Dierker, 2007). The initial skull stripping was performed using a multi-seeded implementation of BET  (Smith, 2002)  optimized for macaque brains, while subsequent brain extraction was based on a high-fidelity template registered to the F99 macaque space.
The RF bias-field was estimated and corrected using a robust implementation of FAST  (Zhang et al. , 2001). Linear and non-linear registration to F99 space was achieved using FLIRT  (Jenkinson et al. , 2002; Jenkinson and Smith, 2001)  and FNIRT  (Andersson et al. , 2007; Jenkinson et al. , 2012)  with configurations adjusted to reflect macaque rather than human brain characteristics. The application of a robust and macaque-optimised version of FAST also resulted inestimated compartments for grey matter, white matter, and meninges with cerebral spinal fluid. For each compartment a posterior-probability map was created by integrating a set of prior probability maps based on 112 Macaca mullata individuals  (McLaren et al. , 2009)  with the dataset-specific evidence provided by FAST. Segmentation of subcortical structures was achieved by registration to the D99 atlas  (Reveley et al.
Macaque rs-fMRI pre-processing The first 5 volumes of the functional EPI datasets were discarded to ensure a steady RF excitation state. EPI timeseries were motion corrected using MCFLIRT. Given that the animals were anaesthetized and their heads were held in a steady position, any apparent image motion, if present at all, is caused by changes to the B0 field and temperature, rather than by head motion. Accordingly, the parameter estimates from MCFLIRT can be considered to be 'B0-confound parameters' instead. Each timeseries was checked rigorously for spikes and other artefacts, both visually and using automated algorithms; where applicable slices with spikes were linearly interpolated based on temporally neighbouring slices. Brain extraction, bias-correction, and registration was achieved for the functional EPI datasets in an iterative manner, similar to the pre-processing of the structural images with the only difference that the mean of each functional dataset was registered to its corresponding T1w image using rigid-body boundary-based registration (FLIRT).
To assess the contribution of the meningeal compartment signal, we quantified the effect of TUS for each point on the cortical surface in data that was not corrected for the meningeal signal (see Macaque rs-fMRI pre-processing). For each point, we calculated it's coupling with the rest of the brain, sorted all Fisher's z-values in ascending order, and extracted the z-value at the 98 th -percentile. Z-values at this level describe relatively strong coupling with the seed point and are often observed in the vicinity of the seed region or other strongly connected regions (depicted in bright yellow in Figure  6 ), ensuring that irrelevant connections are ignored. As such, this simple statistic is wellsuited to capture any main effects of TUS on coupling strength, including those observed when comparing panels (b) and (e) in Figure  6.
, 2014). The relatively small number of TUS sites, animals, and fMRI runs allowed us to exhaustively test all possible permutations (24309) to obtain the true probability of rejecting the null hypothesis. All other statistical inferences were drawn in the context of generalized linear mixed-effects (GLME) models that considered the intercept and factorial design (including interactions where appropriate) as fixed effects and the intercept and slope grouped per animal as random effects with possible correlation between them (as implemented in MATLAB, Mathworks, Natick, USA). The models were assumed to adhere to a normal distribution of the data (not formally tested) and were fitted using Maximum-Pseudo-Likelihood estimation methods where the covariance of the random effects was approximated using Cholesky parameterization.
Figure1. Overview of the experimental design, time-line, pre-processing pipeline, and analysis strategy. Top panel: The same three macaque monkeys (MK1, MK2, MK3) participated in experiments 1 and 2, and a control experiment conducted in the absence of TUS. Three other monkeys (MK4, MK5, MK6) participated in experiment 3. In all experiments anaesthesia was induced ~1. 45 hr before the TUS intervention or no-TUS control. In experiment 1 the 40 s TUS protocol was delivered over the supplementary motor area (SMA), while in experiments 2 and 3, the protocol was targeted at frontal polar cortex (FPC). In all sessions, three consecutive runs of resting fMRI (26 min per run) were acquired starting ~40 min after the TUS or control protocol. Bottom left panel: Data from the resting fMRI runs were pre-processed following a standardized pipeline to address artefacts, improve image quality and signal-to-noise ratio, and prepare for connectivity analyses.
Acknowledgements We would like to thank  Greg Daubney  for his assistance with the histological preparation and analysis, and  Dr. Constantin Coussios  and  Dr. Robin Cleveland  for their technical support. Funding for this research was provided by a  Wellcome Trust   UK Henry Dale fellowship  ( 105651/Z/14/Z ),  Wellcome   Trust Senior Investigator Award  ( WT100973AIA ),  Medical Research Council UK (MRC)  programme grant ( MR/P024955/1 ),  MRC  programme grant ( G0902373 ),  Wellcome Trust   UK Grant  ( 105238/Z/14/ Z ),  Wellcome Trust Sir Henry Wellcome  fellowship ( 103184/Z/13/Z ),  Biotechnology and Biological Sciences Research Council UK (BBSRC)  grant ( BB/N019814/1 ), The  Netherlands Organisation for Scientific Research (NWO)  grant ( 452-13-015 ), The  Wellcome Centre for Integrative Neuroimaging  ( 203139/Z/16/Z ),  Bettencourt Schueller Foundation , and the  Agence Nationale de la Recherche  under the  Future Investments program  ( ANR-10-EQPX-15 ).
Data availability FSL can be downloaded from https://fsl. fmrib. ox. ac. uk/fsl/fslwiki. HCP Workbench can be downloaded from https://www. humanconnectome. org/software/connectome-workbench. For any information regarding MrCat please see http://www. rbmars. dds. nl/lab/toolbox. html; further inquiries can be directed to Lennart Verhagen (lennart. verhagen@psy. ox. ac. uk). All dedicated software tools are available at https://github. com/neuroecology/MrCat. Data contributing to this work have been publicly deposited at the Wellcome Centre for Integrative Neuroimaging (https://git. fmrib. ox. ac. uk/lverhagen/offlinetus). For any inquiries regarding the data, please contact Jerome Sallet (jerome. sallet@psy. ox. ac. uk). The following dataset was generated: Author(s) Year Dataset title Dataset URL Database and Identifier Lennart Verhagen 2019 offlineTUS https://git.
Current work on multimodal machine translation (MMT) has suggested that the visual modality is either unnecessary or only marginally beneficial. We posit that this is a consequence of the very simple, short and repetitive sentences used in the only available dataset for the task (Multi30K), rendering the source text sufficient as context. In the general case, however, we believe that it is possible to combine visual and textual information in order to ground translations. In this paper we probe the contribution of the visual modality to state-of-the-art MMT models by conducting a systematic analysis where we partially deprive the models from source-side textual context. Our results show that under limited textual context, models are capable of leveraging the visual input to generate better translations.
Introduction Multimodal Machine Translation (MMT) aims at designing better translation systems which take into account auxiliary inputs such as images. Initially organized as a shared task within the First Conference on Machine Translation (WMT16)  (Specia et al. , 2016) , MMT has so far been studied using the Multi30K dataset  (Elliott et al. , 2016) , a multilingual extension of Flickr30K  (Young et al. , 2014)  with translations of the English image descriptions into German, French and Czech  (Elliott et al. , 2017; Barrault et al. , 2018). The three editions of the shared task have seen many exciting approaches that can be broadly categorized as follows: (i) multimodal attention using convolutional features  (Caglayan et al. , 2016; Calixto et al. , 2016; Libovický and Helcl, 2017; Helcl et al.
This affects 3. 3% and 3. 1% of the words in the training and the test set, respectively. Entity Masking. The Flickr30K dataset, from which Multi30K is derived, has also been extended with coreference chains to tag mentions of visually depictable entities in image descriptions  (Plummer et al. , 2015). We use these to mask out the head nouns in the source sentences (D N in Table  1 ). This affects 26. 2% of the words in both the training and the test set. We hypothesize that a multimodal system should heavily rely on the images to infer the missing parts. Progressive Masking. A progressively degraded variant D k replaces all but the first k tokens of source sentences with  [v]. Unlike the color deprivation and entity masking, masking out suffixes does not guarantee systematic removal of visual context, but rather simulates an increasingly low-resource scenario.
Experimental Setup Dataset. We conduct experiments on the English→French part of Multi30K. The models are trained on the concatenation of the train and val sets (30K sentences) whereas test2016 (dev) and test2017 (test) are used for early-stopping and model evaluation, respectively. For entity masking, we revert to the default Flickr30K splits and perform the model evaluation on test2016, since test2017 is not annotated for entities. We use word-level vocabularies of 9,951 English and 11,216 French words. We use Moses  (Koehn et al. , 2007)  scripts to lowercase, normalize and tokenize the sentences with hyphen splitting. The hyphens are stitched back prior to evaluation. Visual Features. We use a ResNet-50 CNN  (He et al. , 2016)  trained on ImageNet  (Deng et al. , 2009)  as image encoder.
Results We train all systems three times each with different random initialization in order to perform significance testing with multeval  (Clark et al. , 2011). Throughout the section, we always report the mean over three runs (and the standard deviation) of the considered metrics. We decode the translations with a beam size of 12. 1 github. com/lium-lst/nmtpytorch Figure 1: Entity masking: all masked MMT models are significantly better than the masked NMT (dashed). Incongruent decoding severely worsens all systems. The vanilla NMT baseline is 75. 9 2. We first present test2017 METEOR scores for the baseline NMT and MMT systems, when trained on the full dataset D (Table  2 ). The first column indicates that, although MMT models perform slightly better on average, they are not significantly better than the baseline NMT.
Discussion and Conclusions We presented an in-depth study on the potential contribution of images for multimodal machine translation. Specifically, we analysed the behavior of state-of-the-art MMT models under several degradation schemes in the Multi30K dataset, in order to reveal and understand the impact of textual predominance. Our results show that the models explored are able to integrate the visual modality if the available modalities are complementary rather than redundant. In the latter case, the primary modality (text) sufficient to accomplish the task. This dominance effect corroborates the seminal work of  Colavita (1974)  in Psychophysics where it has been demonstrated that visual stimuli dominate over the auditory stimuli when humans are asked to perform a simple audiovisual discrimination task.
Acknowledgments This work is a follow-up on the research efforts conducted within the "Grounded sequenceto-sequence transduction" team of the JSALT 2018 Workshop. We would like to thank  Jindřich Libovický  for contributing the hierarchical attention to nmtpytorch during the workshop. We also thank the reviewers for their valuable comments. Ozan Caglayan  and  Loïc Barrault  received funding from the  French National Research Agency (ANR)  through the  CHIST-ERA   M2CR  project under the contract  ANR-15-CHR2-0006-01. Lucia Specia  and  Pranava Madhyastha  received funding from the  MultiMT  ( H2020 ERC Starting Grant No. 678017 ) and  MMVC (Newton Fund Institutional Links Grant ,  ID 352343575 ) projects.
In primates, it has been suggested that pair-living species evolved exclusively from solitary  [7]  or from both solitary and group-living ancestors  [8, 9]. In carnivorans, the long-held hypothesis that social evolution involved transitions in social organisation from a solitary ancestor into more advanced forms of group living [solitary ancestor hypothesis: 10, 11] has been questioned  [6]. These inconsistent results likely occurred for several reasons. First, studies have relied on different datasets, methods of analysis, and conceptual frameworks  [3]. In an effort to account for as many species as possible, some studies relied on information from secondary sources and taxonomic inference, such as the untested assumption that members of the same genus share the same social organisation  [12].
(a) Data collection Searches were conducted using Web of Science and Google Scholar to find primary sources reporting social organisation for all 226 extant species of Artiodactyla  [47]. The initial search consisted of the scientific name (genus and species) and a keyword ('social', 'herd', or 'group'). If no sources were found, a final search used only the scientific name. In Web of Science, search results were refined by selecting three research areas: 'zoology', 'behavioral science', and 'environmental science/ecology', and document type 'article'. Lab-based studies, studies in enclosures smaller than 1,000-hectares, and studies that included manipulation of individuals, groups, or resources were discarded. From the 267 primary sources, we coded the following social organisation(s): 1) Solitary, 2) Pair-living, 3) Sex-specific social organisation 4) Unisex groups, 5) Single female/multi-male, 6) Multi-female/single male, and 7) Multi-female/multimale (Supplementary Material Table  S1 ).
Additional details are provided in Supplementary Material S2. (c) Predictor variables Each species was categorized as either seasonal or non-seasonal breeder  [38]. The extent of sexual dimorphism was calculated as the ratio of adult male to female body mass using data reported in Pérez-Barbería & Gordon  [38]. Body size was included as mean adult female body mass. Habitat type was derived from the primary source and categorized based on IUCN classification (www. iucn. org) as desert, forest, rocky areas, savanna, grassland, shrubland, wetlands, or artificial.
(d) Phylogeny We used the mammal supertree from Bininda-Emonds et al. [48]. Some species names in the database had to be amended to match the phylogeny as detailed in the accompanying R code. In virtually all cases, a name mismatch could be resolved by finding a pseudonym for that species through www. iucn. org, or by using a sister species that was not included in the database. In one case, two closely-related taxa missing from the supertree (Moschus leucogaster and Moschus cupreus) were proxied by the same sister species (Moschus chrysogaster).
(e) Statistical analysis We used Bayesian phylogenetic mixed-effects models, accounting for the multilevel structure of the data (populations nested within species) and the phylogenetic relationships among species  [24, 27]. Predictors included sexual dimorphism, female body size, breeding seasonality, and number of habitats. Type of habitat was modeled as a random intercept. All models controlled for research effort by including the number of studies. To control for potential geographical biases continent was included as a random intercept. Prior to fitting the model, we estimated the likely ancestral state for body size, sexual dimorphism, and breeding seasonality (see Supplementary Material 3). We then centered body size and sexual dimorphism on these estimated ancestral states and chose the likely ancestral breeding seasonality as the reference category.
Phylogenetic signal was calculated as the proportion of variance captured by the phylogenetic random effect(s) following  [57]. All models converged as assessed with the potential scale reduction factor (all =<1. 01), effective sample sizes (all >500), and by visually examining trace plots of the Markov chains. Details on model fitting can be gleaned from the accompanying R code (available at https://github. com/adrianjaeggi/artiodactyl. socialorg).