dataesr/scientific-paragraphs-categorization huggingface dataset:
An interesting result is obtained when combining the two: GA-IPA reaches almost the same TP rate (80-90%) as with the data set considered in Fig  4 , which however had only an average of 11 paralogs per species. Besides, for this dataset, our approach performs substantially better than MirrorTree-based variants, see Table C in S1 Text. We conclude that GA-IPA is very robust to high paralog multiplicities. This provides a major improvement over previous approaches, and extends the applicability of paralog pairing to highly amplified protein families, cf. the Introduction. The second difficult case we consider is the case of small MSAs (i. e. MSAs with small M). To analyse this case, we randomly subsampled the species in the full HK-RR data set (see Materials and methods) to obtain smaller data sets, some being as small as M = 100 sequences.
As can be seen in Fig  5B , the IPA without seed co-MSA has a strong M dependence, and several thousands of sequences are needed in each MSA to reach high TP rates above, e. g. , 70-80%. For small M, GA performs a bit better, and its TP rate increases faster, outperforming the IPA by a factor larger than two for M on the order of a few hundreds. However, GA yields smaller TP rates for larger M (at constant k for the kNN graph), and GA performs substantially less well than the IPA for M on the order of a few thousands. Indeed, when M increases, more and more edges of the similarity network link distinct species in the two families, making the networks difficult to align. Crucially, we find that the combined GA-IPA always performs best, thus getting the best of both worlds. For very small data sets, GA-IPA does not lead to substantial accuracy gains over GA alone, because DCA needs sufficient data for accurate inference.
Data sets We consider as our primary benchmark a data set composed of 23,632 pairs of natural sequences of interacting histidine kinases (HK) and response regulators (RR) from the P2CS database  [42, 43] , as previously described in  [34, 38]. In this data set, interacting partners are determined using proximity in the genome, derived from the annotations of the P2CS database. This allows us to assess partner inference performance in this natural data sets as well as in derived ones. Discarding the 208 pairs from species with only one such pair for which pairing is trivial yields a dataset of 23,424 HK-RR pairs with 11. 1 paralogs per species on average. In our first benchmark, we focus on a smaller "standard dataset" extracted from this complete dataset, in view of computational time constraints.
The standard dataset was constructed by picking species randomly. It comprises 5064 pairs from 459 species comprising at least two HK-RR pairs, with an average number of pairs per species hm p i = 11. 03 [34]. To assess the impact of the number of HK-RR pairs per species on the success of GA-IPA, we constructed another dataset where the species with the highest numbers of pairs (25 to 41 in practice) were picked, yielding a dataset with M = 5052 pairs and hm p i = 29. 20  [34]. In both datasets, the actual paralog numbers vary strongly from species to species, going from the imposed minimum of two paralogs up to a maximum of 42 paralogs, cf. the histograms in Fig A in S1 Text. Finally, to assess the impact of varying M on the performance of inference by GA-IPA, we constructed smaller data sets by picking species randomly from the full data set  [34].
We also consider a data set comprising 17,950 pairs of ABC transporter proteins homologous to the Escherichia coli MALG-MALK pair of maltose and maltodextrin transporters  [12, 34]  and extract a dataset of *5000 pairs from it. Similarly, we consider the homologs of interacting E. coli enzymes XDHA-XDHC, and retain the full dataset of *2000 pairs in this case. In these data sets, interacting partners are determined using proximity in the genome (as for HK-RR), following the approach from Ref. [12]. Since the approach presented here explicitly relies on phylogeny, it is interesting to also test it on proteins that share phylogeny without being interacting partners or having common functional While it is difficult to be certain that two protein families do not have common functional constraints, we picked two pairs of families that are encoded in close proximity on prokaryotic genomes but do not have known physical interactions  [44].
Pseudocode for the simulated annealing procedure is given below. Algorithm: Simulated annealing 0. Initialize the matching π by randomly pairing each sequence A with a sequence B from the same species. Initialize temperature and number of steps: T = T 0 and t = 0. 1. Propose MCMC updates at temperature T, until a fixed number are accepted. Each proposed update proceeds as follows: *Randomly select one pair of sequences from the full dataset. * Randomly select a second pair of sequences from the same species. * Propose to exchange their interaction partners, yielding a new matching π 0. * Update the matching to π 0 with probability min f1; exp½ðCðpÞ À Cðp 0 ÞÞ=T�g.
It relies on the same iterative procedure, but uses scores based on mutual information instead of inferring DCA models. The MI-IPA reaches performances slightly higher the DCA-IPA on natural datasets, and is more robust to smaller datasets  [38]. Matlab implementations of the DCA-based IPA and the MI-based IPA on our standard HK-RR dataset are freely available at https://doi. org/10. 5281/zenodo. 1421861 and https://doi. org/10. 5281/zenodo. 1421781, respectively. Julia implementations of the GA-IPA (as well as of the IPA), both DCA-based and MI-based are freely available at https://doi. org/10. 5281/ zenodo. 7731108.
Distance measures were corrected for multiple hits allowing that distances can be more than 1 via multiple substitution per site  [23]  by using protdist from the phylip  [49]  package instead of ClustalW. Also,  [50]  incorporates information on the overall evolutionary histories of the species to correct distances between orthologues due to speciation events. While these methods often restrict to orthologs, some variants tackle the paralog pairing problem  [19] [20] [21] [22] [23] [24] [25] [26] [27] [28]. In several of these methods  [19-21, 23, 24] , the order of the sequences in one of the two families is modified to make the two distance matrices more similar. Similarity can be assessed e. g. via the Pearson correlation between the entries of the two distance matrices  [21]. In this framework, consider the matrix d A (resp.
CAGP and MW acknowledge funding by the  EU H2020 Research and Innovation Programme MSCA-RISE-2016  (grant agreement No. 734439  InferNet). AFB acknowledges funding by the  European Research Council (ERC)  under the  EU H2020 Research and Innovation Programme  (grant agreement No. 851173 ). AFB and MW thank the  Institut de Biologie Paris-Seine (IBPS) at Sorbonne Universite  ´for funding via a  Collaborative Grant  ( Action Incitative). This work was performed in part at the  Aspen Center for Physics , which is supported by  National Science Foundation  grant  PHY-1607611. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
FP white matter tracts connecting contacts in the right hemisphere To specify the anatomical connections between contacts within each cluster, we performed white matter tractography analysis paired with probability maps in 176 healthy individuals from the Human Connectome database  (34). Fig. 4  displays the white matter tracts connecting frontal contacts and parietal contacts within each cluster. This result suggests that our unsupervised cluster analysis mapped on existing anatomo-functional networks  (27). We examined the frontal association tracts described in Ref. (35) , and found that our frontal and parietal contacts were connected by branches of the superior longitudinal fasciculus (threshold-free cluster enhancement-based non-parametric t-test, p < 0. 05). Specifically, the Sustained cluster was mainly connected by the right superior longitudinal fasciculus (SLF) II (43.
Behavioral analysis For each participant, we first excluded trials with response time (RT) faster than 150ms or exceeding three standard deviations. Statistical tests in behavioral analysis were performed using JASP software (version 0. 16. 0. 0, https://jasp-stats. org/). We performed a two-way ANOVA with the factors of cue validity and conscious report (in the detection task) on the discrimination accuracy. RTs analysis was not reported since participants had to wait for the response screen to give their responses. Using a nonparametric measure (57), we conducted Signal Detection Theory (SDT) analysis (  29 ) to evaluate the bias produced by the cue validity on participants' perceptual sensitivity a'. We computed the mean percentage of seen targets when the Gabor was presented (Hits) and when the Gabor was absent (false alarms; FA).
nmr. mgh. harvard. edu/) embedded in the Epiloc toolbox, followed by a manual verification and correction, if necessary. Fig. 1C  and Table  S1  show the localization of usable contacts referring to Desikan-Killiany-Tourville atlas parcellation (59): 29 contacts (4%) from occipital, 332 (46%) from temporal, 78 (11%) from parietal, 202 (28%) from frontal, 24 (3%) subcortical and 62 (9%) in white matter. There were 288 contacts in the left hemisphere and 439 contacts in the right hemisphere. Signal preprocessing was conducted using Matlab (R2018b, The MathWorks, Inc. ) and FieldTrip toolbox  (60). First, all signals were down sampled to 512 Hz and all contacts were rereferenced to their adjacent neighbor contact on the same electrode, yielding a bipolar montage, in order to ensure that iEEG signals could be considered as originating from a cortical volume centered within the two contacts.
White matter tracts computing We dissected white matter tracts connecting parietal contacts with frontal contacts. We modeled each intracerebral contact by a 3 mm-sphere. In each cluster, we created two region-of-interests (ROIs), respectively consisting of frontal contacts and parietal contacts. The parcellation of contacts was referred to Desikan-Killiany-Tourville atlas (see details in iEEG preprocessing and Table  S1  for contact numbers). We used a dataset that includes 176 preprocessed healthy individuals tractography acquired at 7 tesla by the Human Connectome Project team  (34). We performed tracts filtering in TrackVis toolbox  (62)  to obtain tracts connecting frontal contacts and parietal contacts in frontal association tracts  (35)  (three branches of the superior longitudinal fasciculus; uncinate; long and anterior segments of the arcuate fasciculus; inferior frontooccipital fasciculus).
Acknowledgments: We thanks  Michel Thiebaut de Schotten  for the help in white matter tracking analysis. Funding:  Agence Nationale de la Recherche  grant  ANR-16-CE37-0005   Agence Nationale de la Recherche  grant  ANR-10-IAIHU-06   Fondation pour la Recherche sur les AVC  grant  FR-AVC-017   ICM-OCIRP Fund from Dassault Systèmes.
, 2014)  is employed. For wave tank testing, the real-time control algorithms are implemented using the Matlab/Simulink xPC environment. As a result, we believe that this system, with a standard simulation environment and validated model, standard maintained prototype WEC and PTO system, and connected real-time control computer with a popular programming interface (MATLAB/Simulink), provides an ideal benchmark WEC control platform, with some evidence of its success in this regard documented in Section 10. The original WECCCOMP parameters were announced in  Ringwood et al. (2017) , with the results from the simulation stage of the competition subsequently presented in  Ringwood et al. (2019). This paper now documents the comparative results from the experimental stage of the competition, where competitors were assigned 2.
WEC system simulation For the simulation stage of WECCCOMP, a model of the Wavestar device with control was implemented in WEC-Sim. WEC-Sim is an opensource code jointly developed by Sandia National Laboratories and the National Renewable Energy Laboratory, through funding from the U. S. Department of Energy's Water Power Technologies Office  (WEC-Sim, 2018). The WEC-Sim code is developed in MATLAB/Simulink, and uses Simscape Multibody to solve for a WEC's rigid body dynamics. WEC-Sim's implementation is a collection of MATLAB scripts (*. m files) and Simulink libraries (*. slx files), which are hosted on an opensource GitHub repository (WEC-Sim-Github, 2018), with v4. 0 released in September 2019. WEC-Sim is a time-domain code that solves for the system dynamics of WECs consisting of a combination of rigid bodies, PTO systems, mooring systems, and control systems.
The dynamic response in WEC-Sim is calculated by solving the WEC's equation of motion for each rigid body about its centre of gravity, in 6 DOFs, based on Cummins' formulation  (Cummins, 1962). The WEC-Sim source code includes a preprocessing BEM input/ output code that imports hydrodynamic data generated by the potential flow solvers WAMIT, NEMOH, or AQWA, and parses the BEM data into a (*. h5) data structure that is read by WEC-Sim. For more information about WEC-Sim theory, implementation, functionality, and application, refer to the WEC-Sim website  (WEC-Sim, 2018). In relation to sea state realisation, 499 bins of equal energy are selected that are added together in a multi-sine and given random phases. The contestants were instructed to use a constant phase seed for each sea state and was used to run the competitors controller so the same wave elevation was used in the evaluation.
Implementation environment The main objective of the control competition is to test, verify, and calibrate control algorithms on the prototype hardware, for the control objective documented in Section 3. To achieve this objective, a rapid control prototyping (RCP) architecture is used, which facilitates the importation of a controller, which has been tested in a numerical environment, into a real-time operating system connected to a real-world input/output interface. This step is of paramount importance, since any numerical model is only an approximation of the corresponding real-world system. The RCP architecture is implemented in the Matlab/Simulink (version R2015b) environment, using the xPC Target toolbox, with the RCP architecture sketched in Fig. 4. The hardware WEC comprises sensors, floater, mechanical and structural elements, linear actuator and related controllers.
The WEC is interfaced to the Target PC via an I/O board. The Target PC runs a hard real-time operating system (OS) and embeds the controller under development. The base target PC sample frequency is 1 kHz, while the controller sample frequency depends upon the computational cost of the controller itself, e. g. a simple linear damping control can run as high as 1 kHz. Nevertheless, it should be noted that the sampling frequency of the controller must be an integer divider of the base sampling frequency in the Target PC. The Target and Host PCs communicate through a local intranet connection. The controller under development is implemented in the Host PC, using a Simulink block diagram and then deployed on the Target PC as a compiled code segment. Once running, the controller parameters are accessible (modifiable) from the Host PC.
The output of the CONTROLLER block (line 3) is the force/position used as reference for the internal force/position control loop within the signal output interface block. For sake of simplicity, the interface of the controller block is summarised in Table  3  It is important to note that not all the Simulink libraries and Matlab functions are compatible with the xPC toolbox, due to limitations in the compiler engines; a list of supported toolboxes and functions can be found in the Matlab-R2015b documentation or at https://se. mathworks. com/products/compiler/supported/compiler_support. html. This provided a few surprises, with limited opportunity for reworking code in the narrow implementation time window, and could have led to (unquantified) disparities between controller implementations in simulation and tank tests.
Experimental testing The first step of experimental testing was to run some simple system identification tests. Step inputs of various magnitudes were applied to the motor, in the absence of wave excitation. Results from these tests were utilised to ensure the data acquisition system and motor control were operating as expected. Next, a series of damping control tests were run. A PTO force proportional to PTO velocity was applied for all wave cases. This served two purposes: (a) To provide a performance baseline to compare MPC results to, and (b) For use in tuning the estimation and wave prediction algorithms. The damping coefficients for each wave case were selected to maximise power output for each wave case, based on WEC-Sim simulations performed prior to the test. Excitation force tests were performed on a subset of the sea states.
These tests were performed by mechanically restricting the motion of the WEC. The resulting force measured by the load transducer is the excitation force. The results of these tests were used to provide a 'true' excitation force that could be used to tune the estimator and predictor models. The process and measurement covariance matrices used in the Kalman Filter were then tuned offline. This process was performed by using the experimental measurements as inputs to the filter, and comparing the estimated excitation force values to the experimental excitation force data from the excitation tests. The covariance matrices were adjusted until the difference between the estimated and measured excitation force was acceptable. The prediction model was tuned using the estimated excitation force from the experimental damping tests ( F𝑒 ), and the up-wave elevation (𝜂 3 ), as inputs.
Acknowledgment This work was supported by  Science Foundation Ireland  under Grant No. SFI/13/IA/1886  and through the  Research Centre for Energy, Climate and Marine (MaREI)  under Grant No. 12/RC/2302_P2. The research leading to these results has also received funding from the  European Union   Horizon 2020 Framework Programme  ( H2020 ) under  Marinet   2  Grant No. 731084. This work was co-authored by the  National Renewable Energy Laboratory , operated by  Alliance for Sustainable Energy, LLC ,for the  U. S. Department of Energy (DOE)  under Contract No. DE-AC36-08GO28308. Funding was also provided by the  U. S. Department of Energy, USA, Office of Energy Efficiency and Renewable Energy, USA, Water Power Technologies Office, USA. The views expressed in the article do not necessarily represent the views of the  DOE  or the  U.
D. GRAPHICAL USER INTERFACE (GUI) BASED ON MATLAB We have programmed a Matlab based acquisition graphical user interface (GUI) to control StarLab and VNA with a commercial PC. This GUI uses an application programming interface (API) provided by MVG and the virtual instrument standard architecture (VISA) based GPIB interface to control the StarLab and VNA, respectively. The controllable VNA measurement parameters are: start and stop of frequency of sweep, number of points, IF bandwidth, sweep averaging, and selection of S parameters. The rest of the parameters (such as VNA output power, etc) are programmed directly on the VNA if required. The controllable StarLab measurement parameters are probe array, the polarization of probes, the sweep of specific probes, azimuth angle φ using azimuth motor, and electronic scanning of elevation angle θ.
Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases). However, these models are still considered unreliable by clinicians due to unresolved issues concerning i) the temporal consistency of their predictions, and ii) their ability to generalize across datasets. In this context, we propose a comprehensive comparison between the current best performing methods in medical/echocardiographic image segmentation, with a particular focus on temporal consistency and cross-dataset aspects. We introduce a new private dataset, named CARDINAL, of apical two-chamber and apical four-chamber sequences, with reference segmentation over the full cardiac cycle.
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use? 1 Introduction Echocardiographic imaging has undergone major advances in recent years thanks to artificial intelligence, especially the deep learning (DL) paradigm. In particular, the automated extraction of clinical indices from the segmentation of cardiac structures has been the subject of intense research leading to major breakthroughs. A key component of these advances has been the publication of open access annotated datasets, including CETUS (45 patients, 3D images annotated at End-Diastole -ED and End-Systole -ES)  [1] , CAMUS (500 patients, 2D images annotated at ED and ES in apical two-chamber -A2C -and apical four-chamber -A4C -views)  [5] , EchoNet-Dynamic (10,036 patients, 2D sub-sampled images annotated at ED and ES in A4C view)  [7] , HMC-QU (109 patients, 2D sequences annotated in A4C view)  [2]  and TED (98 patients from the CAMUS dataset, 2D sequences annotated in A4C view)  [8].
These datasets allowed effective and fair comparisons of methods, whether they are generic image segmentation models  [5, 6]  or were specifically designed to process echocardiographic images  [11, 9]. Thus, the performance of current state-of-the-art (SOTA) methods on the CAMUS dataset confirmed the dominance of the DL-based methods, which finally achieved inter-and intra-observer variability for most of the geometric (Dice score, Hausdorff distance, mean absolute distance -MAD) and clinical metrics (ejection fraction -EF, volumes at ED/ES). Although these results are extremely promising and represent a crucial step towards the automation of echocardiographic image analysis, they are not sufficient to justify confidence in fully automated methods in a clinical context. Indeed, two crucial challenges on the path to the practical application of these algorithms remain understudied in the field: i) the frame-by-frame temporal consistency of the predictions, and ii) the generalization of the methods across datasets.
Based on this observation, we propose the following contributions: 1. We study the performance of two generic architectures based on common temporal data processing techniques on 2D echocardiography sequences, and compare them to current SOTAs in the same field; 2. We present a new private dataset called CARDINAL (240 patients, 2D sequences annotated in A4C and A2C views), and report the performance impact of training our methods exclusively on CARDINAL and testing on CAMUS.
2D DL Methods The currently best performing method on the CAMUS dataset exploits the nnU-Net formalism  [6]. This model is based on the U-Net architecture and implements several successful DL tricks, such as a patch-wise approach to preserve image resolution, data augmentation during both training and inference to enforce generalization and automatic hyperparameter search of the U-Net architecture to increase accuracy. Note that the 2D version uses only one U-Net model  [4]. Recently, Sfakianakis et al. [9]  developed a DL solution called GUDU based on three key aspects. First, they proposed to use data augmentations tailored to ultrasound acquisition, i. e. variation of the contrast between the myocardial tissue and the left ventricular (LV) cavity, random rotation from the origin of the sectorial shape to mimic different probe positioning, and perspective transformations to simulate probe twisting.