dataesr/scientific-paragraphs-categorization huggingface dataset:
Translators should be made available. • Make the workshop and subsequent work as participatory as possible including the planning of the workshop. Set up an emailing list to facilitate communication, continue with ontology development online and provide regular feedback. • Use real datasets from the community to test the ontology for coverage and accuracy. Encourage the community to start applying the ontology to their datasets and to provide feedback. • Establish a plan for how to update/modify the ontology as our knowledge base changes over time. • Have plans in place to disseminate the workshop proceedings in preparation of the releasing of the ontology, through publications and reports. Report on progress regularly to contributors, funders and other stakeholders.
Conclusions The SCD Ontology workshop brought together thirty six experts in SCD from all over the world. The enthusiasm and active participation of all attendees was noticeable and infectious. The key challenge will be to maintain this momentum to enable completion and implementation of the ontology. Once completed, we anticipate that the ontology will be the most comprehensive collection of knowledge in the SCD field. It will be used to facilitate exploring of new scientific questions and ideas; to facilitate seamless data sharing and collaborations including meta-analysis within the SCD community; and to support the development and curation of databases and clinical informatics in SCD. Most importantly, it is hoped that the ontology will be used to share data among the SCD community, and enable queries across the different datasets.
Acknowledgments The workshop was funded by the  National Heart, Lung, and Blood Institute (NHLBI) ,  National Institutes of Health (NIH)  as a supplement to the  H3ABioNet grant. H3ABioNet  is supported by the  National Human Genome Research Institute (NHGRI) , Office of the Director (OD),  NIH  under award number  U41HG006941. The content of this.
Execution Like in the previous year, SYNTCOMP 2016 was run at Saarland University, on a set of identical machines with a single quad-core intel Xeon processor (E3-1271 v3, 3. 6GHz) and 32 GB RAM (PC1600, ECC), running a GNU/Linux system. Each node has a local 480 GB SSD that can be used as temporary storage. Also like in previous years, the competition was organized on the EDACC platform  [4] , with a very similar setup. To ensure a high comparability and reproducability of our results, a complete machine was reserved for each job, i. e. , one synthesis tool (configuration) running one benchmark. Olivier Roussel's runsolver  [41]  was used to run each job and to measure CPU time and wall time, as well as enforcing timeouts. As all nodes are identical and no other tasks were run in parallel, no other limits than a timeout of 3600 seconds (CPU time in sequential mode, wall time in parallel mode) per benchmark was set.
Like last year, we used wrapper scripts to execute solvers that did not conform completely with the output format specified by the competition, e. g. , to filter extra information that was displayed in addition to the specified output. The model checker used for checking correctness of solutions for the AIGER/Safety track is IIMC 8  in version 2. 0. This year, we also allowed the safety solvers to additionally output a winning region of the safety game, which should be an inductive invariant for the synthesized circuit. If this information was supplied, then we first used an invariant check to determine correctness of the solution, and used full model-checking as a fallback solution if the invariant check failed. For the TLSF/LTL track, the model checker used was V3 9   [48].
Strategy extraction in AbsSynthe uses the co-factor-based approach of Bloem et al. [7] , with some additional optimizations as described in the SYNTCOMP 2014 report  [27]. To facilitate verification of solutions, AbsSynthe also produces the winning region and includes it in the solution file. The algorithms used in AbsSynthe have been described in more detail by Brenguier et al. [12, 13]. Implementation, Availability. AbsSynthe is implemented in C++, and depends on the AIGER toolset  11 and the CUDD package for BDD manipulation (v2. 5. 1)  [45]. The code is available at https://github. com/gaperez64/AbsSynthe/tree/native-dev-par. Demiurge 1. 2. 0 Demiurge was submitted by R. Könighofer and M. Seidl, and competed in both the realizability and the synthesis track. Demiurge implements different SAT-based methods for solving the reactive synthesis problem.
This year, Demiurge competed in exactly the same version as last year. For a more information on Demiurge, we refer to the original descriptions of the implemented algorithms  [6, 44] , as well as the SYNTCOMP 2015 report  [28]. The code is available at http://www. iaik. tugraz. at/content/research/design_verification/ demiurge/ under the GNU Lesser General Public License version 3.
The first two configurations are essentially identical to the configurations that entered SYNTCOMP 2015, and the third one is new. Implementation, Availability. Simple BDD Solver is written in the Haskell functional programming language. It uses the CUDD package for BDD manipulation (updated to v3. 0. 0 this year) and the Attoparsec Haskell package for fast parsing. 13 Altogether, the solver, AIGER parser, compiler and command line argument parser are just over 300 lines of code. The code is available online at: https://github. com/adamwalker/syntcomp.
This translator has been integrated into the SyFCo tool. • an encoding of the Mealy machines constructed during synthesis into AIGER circuits that conform to the SYNTCOMP rules. This encoding uses the Speculoos tools  17  to parse the native Mealy machine output and generate a transducer in AIGER format. Implementation, Availability. Acacia4Aiger is written in Python and C. It uses LTL2BA 18   [23]  to convert LTL specifications into Büchi automata. It also includes the Speculoos and SyFco tools, as mentioned above. The code is available online at: https://github. com/gaperez64/acacia4aiger.
BoSy supports both Mealy and Moore semantics natively, as well as the extraction of a winning strategy for the environment in case of unrealizable specifications. Implementation, Availability. BoSy is written in Swift. It uses LTL3BA 20   [3]  to convert LTL specifications into Büchi automata. It uses bloqqer, RAReQS 21 and QuAbs 22 to solve QBF constraints, as mentioned above. Finally, it uses SyFCo to translate TLSF specifications to its native input format, and ABC to simplify solutions. The code is available online at: https://www. react. uni-saarland. de/tools/bosy/. SYNTCOMP 2016.
When the SMT solver finds a satisfying assignment to the constraints, the solution is first encoded into Verilog, and then converted to AIGER and simplified using existing tools. Implementation, Availability. PARTY-Elli is written in Python. It uses LTL3BA to convert LTL specifications into Büchi automata. It uses Z3  23   [38]  for solving SMT constraints. For converting generated Verilog code into AIGER, it uses vl2mv (contained in VIS  24   [10]  and ABC. Finally, it uses SyFCo to translate TLSF specifications to its native input format. The code is available online at: https://github. com/5nizza/party-elli.
TLSF/LTL-Track: Synthesis In the track for synthesis from LTL specifications in TLSF, participants had to solve the same benchmarks as in the LTL/TLSF-realizability track, except that we removed instances that remained unsolved in the realizability track. Thus, the benchmark set for the synthesis track contains 185 instances. The track had the same participants as the LTL/TLSF-realizability track: Acacia4Aiger with one configuration, BoSy with four configurations, and PARTY-Elli with one configuration. Legacy tool UNBEAST did not run in this track, since it would have been an additional implementation effort to encode its native output into AIGER. As for the AIGER/safety-track, the ranking is only based on the number of instances that can be solved within the timeout, and a solution for a realizable specification is only considered correct if it can be model-checked within a separate timeout of one hour (cf.
Acknowledgments. We thank  Rüdiger Ehlers ,  Ioannis Filippidis ,  Andrey Kupriyanov ,  Kim Larsen ,  Nir Piterman , and  Markus Rabe  for interesting suggestions for the future of SYNTCOMP. The organization of SYNTCOMP 2016 was supported by the  Austrian Science Fund (FWF)  through project  RiSE  ( S11406-N23 ) and by the  German Research Foundation (DFG)  through project " Automatic Synthesis of Distributed and Parameterized Systems " ( JA 2357/2-1 ), and its setup and execution by the  European Research Council (ERC)  Grant  OSARES  (No. 683300 ). The development of AbsSynthe and Acacia4Aiger was supported by an  F. R. S. -FNRS  fellowship, and the  ERC inVEST  ( 279499 ) project. The development of Demiurge was supported by the  FWF  through project  RiSE  ( S11406-N23 ,  S11408-N23 ).
Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks.
The new version of the method has the same objectives as the older version  (Brendel et al. , 2011) , but is more principled, more flexible, and has an analytical solution, meaning that it does not suffer from any numerical optimization problems. Furthermore, the new mathematical formulation highlights similarities to and differences from related well-known methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). The dPCA code is available at http://github. com/machenslab/dPCA for Matlab and Python. eLife digest Many neuroscience experiments today involve using electrodes to record from the brain of an animal, such as a mouse or a monkey, while the animal performs a task. The goal of such experiments is to understand how a particular brain region works. However, modern experimental techniques allow the activity of hundreds of neurons to be recorded simultaneously.
The tool breaks down the activity of a population of neurons into its individual components. Each of these relates to only a single aspect of the task and is thus easier to interpret. Information about stimuli, for example, is distinguished from information about the animal's confidence levels. Kobak, Brendel et al. used the demixing tool to reanalyse existing datasets recorded from several different animals, tasks and brain regions. In each case, the tool provided a complete, concise and transparent summary of the data. The next steps will be to apply the analysis tool to new datasets to see how well it performs in practice. At a technical level, the tool could also be extended in a number of different directions to enable it to deal with more complicated experimental designs in future.
In a related approach, the firing rates of each neuron at each time point are linearly regressed on stimulus and decision (Figure  1f )  (Brody et al. , 2003). Mante et al. (2013)  suggested to use the regression coefficients of all N neurons (Figure  1g ) as weights to form linear combinations of PSTHs representing stimulus and decision tuning (Figure  1h ). This approach, which the authors call 'targeted dimensionality reduction' (TDR), also yields one component per task parameter: in our example, we obtain one component for the stimulus and one for the decision (Figure  1h ; see Materials and methods for details). Both of these approaches are supervised, meaning that they are informed by the task parameters. At the same time, they do not seek to faithfully represent the whole dataset and are prone to losing some information about the neural activities.
In this manuscript, all datasets will have three parameters: time, stimulus, and decision, and we will decompose the neural activities into five parts: condition-independent, stimulus-dependent, decisiondependent, dependent on the stimulus-decision interaction, and noise (see Figure  8  in the Materials and methods):   (Romo et al. , 1999). (a) Cartoon of the paradigm, adapted from  Romo and Salinas (2003). (b) Demixed principal components. Top row: first three condition-independent components; second row: first three stimulus components; third row: first three decision components; last row: first stimulus/decision interaction component. In each subplot, the full data are projected onto the respective dPCA decoder axis, so that there are 12 lines corresponding to 12 conditions (see legend).
In a nutshell, the loss function ensures that each set of decoder/encoder axes reconstructs the individual, parameter-specific terms, X f , thereby yielding proper demixing, and the data decomposition ensures that the combination of all decoder/encoder pairs allows to reconstruct the original data, X. There are a few other technical subtleties (see Materials and methods for details). (1) We formulated dPCA for simultaneously recorded neural activities. However, all datasets analyzed in this manuscript have been recorded sequentially across many sessions, and so to apply dPCA we have to use 'pseudo-trials'. (2) Similar to any other decoding method, dPCA is prone to overfitting and so we introduce a regularization term and perform cross-validation to choose the regularization parameter. (3) The data and variance decompositions from above are exact only if the dataset is balanced, i.
Somatosensory working memory task in monkey PFC We first applied dPCA to the dataset presented above  (Romo et al. , 1999; Brody et al. , 2003) , encompassing 832 neurons from two animals. As is typical for PFC, each neuron has a distinct response pattern and many neurons show mixed selectivity (some examples are shown in Figure  1b ). Several previous studies have sought to make sense of these heterogeneous response patterns by separately analyzing different task periods, such as the stimulation and delay periods  (Romo et al. , 1999; Brody et al. , 2003; Machens et al. , 2010; Barak et al. , 2010) , the decision period  (Jun et al. , 2010) , or both  (Hernandez et al. , 2010). With dPCA, however, we can summarize the main features of the neural activity across the whole trial in a single figure (Figure  3 ).
The first stimulus component (#5) looks similar to the stimulus components that we obtained with standard regression-based methods (Figure  1e, h ) but now we have further components as well. Together they show how stimulus representation evolves in time. In particular, plotting the first two stimulus components against each other (see Video 1) illustrates how stimulus representation rotates in the neural space during the delay period so that the encoding subspaces during F1 and F2 periods are not the same (but far from orthogonal either). As explained above, the demixed principal axes are not constrained to be orthogonal. The angles between the encoding axes are shown in Figure  3e , upper-right triangle; we discuss them later, together with other datasets. Pairwise correlations between components are all close to zero (Figure  3e , lower-left triangle), as should be expected since the components are considered to represent independent signals.
One notable difference from before is the presence of strong interaction components in Figure  4b. However, these interaction components are in fact stimulus components in disguise. In match trials, S2 and S1 appear at the same location, and in non-match trials at opposite locations. Information about S2 is therefore given by a non-linear function of stimulus S1 and the trial type (i. e. decision), which is here captured by the interaction components. Here again, our analysis summarizes previous findings obtained with this dataset. For instance, the first and the second decision components show tuning to the match/non-match decision during the S2 period and in the subsequent delay period. Using these components as fixed linear decoders, we achieve single-trial classification accuracy of match vs.
non-match of 75% for t > 2 (cross-validated, see Materials and methods, Figure  12 ), which is approximately equal to the state-of-the-art classification performance reported previously  (Meyers et al. , 2012). Constantinidis et al. have also recorded population activity in PFC before starting the training (both S1 and S2 stimuli were presented exactly as above, but there were no cues displayed and no decision required). When analyzing this pre-training population activity with dPCA, the first stimulus and the first interaction components come out close to the ones shown in Figure  4 , but there are no decision and no 'memory' components present (data not shown), in line with previous findings  (Meyers et al. , 2012). These task-specific components appear in the population activity only after extensive training.
2-0. 5 s). We analyzed the activity of 437 neurons recorded in five rats in four conditions: two stimuli (left and right) each paired with two decisions (left and right). Two of these conditions correspond to correct (rewarded) trials, and two correspond to error (unrewarded) trials. Since the task was self-paced, each trial had a different length; in order to align events across trials, we restretched (time-warped) the firing rates in each trial (see Materials and methods). Alignment methods without time warping led to similar results (data not shown). Just as neurons from monkey PFC, neurons in rat OFC exhibit diverse firing patterns and mixed selectivity  (Feierstein et al. , 2006). Nonetheless, dPCA was able to demix the population activity (Figure  5 ). In this dataset, interaction components separate rewarded and unrewarded conditions (thick and thin lines in Figure  5b , bottom row), i.
3-2 s. We analyzed the activity of 214 OFC neurons from three rats recorded in 8 conditions, corresponding to four odor mixtures, each paired with two decisions (left and right). During the presentation of pure odors (100% right and 100% left) rats made essentially no mistakes, and so we excluded these data from the dPCA computations (which require that all parameter combinations are present, see Discussion). Nevertheless, we displayed these additional two conditions in Figure  6. The dPCA components shown in Figure  6b  are similar to those presented in Figure  5b. Here again, some of the interaction components (especially the second one, #5) show strong tuning already during the anticipation period, i. e. before the actual reward delivery. The inset in Figure  6b  shows the mean value of the component #5 during the anticipation period, separating correct (green) and incorrect (red) trials for each stimulus.
The characteristic U-shape for the error trials and the inverted U-shape for the correct trials agrees well with the predicted value of the rat's uncertainty in each condition  (Kepecs et al. , 2008). Accordingly, this component can be interpreted as corresponding to the rat's uncertainty or confidence about its own choice, confirming the results of  Kepecs et al. (2008). In summary, both the main features of this dataset, as well as some of the subtleties, are picked up and reproduced by dPCA.
Universal features of the PFC population activity One of the key advantages of applying dPCA to these four datasets is that we can now compare them far more easily than was previously possible. This comparison allows us to highlight several general features of the population activity in prefrontal areas. First, most of the variance of the neural activity is always captured by the condition-independent components that together amount to 65-90% of the signal variance (see pie charts in Figures  3-6d ; see Materials and methods for definition of 'signal variance'). These components capture the temporal modulations of the neural activity throughout the trial, irrespective of the task condition. Their striking dominance in the data may come as a surprise, as such condition-independent components are usually not analyzed or shown (cf.
, 2011; Rishel et al. , 2013; Raposo et al. , 2014). Nevertheless, many pairs were significantly non-orthogonal, meaning that neurons expressing one of the components tended to also express the other one. Throughout the four datasets, we identified 277 pairs of axes (among the first 15 axes) corresponding to different parameters. Of these, 38, i. e. 14%, were significantly non-orthogonal with p<0:001 (8 out of 53 if we do not take time axes into account). Third, all dPCA components in each of the datasets are distributed across the whole neural population (as opposed to being exhibited only by a subset of cells). For each component and each neuron, the corresponding encoder weight shows how much this particular component is exhibited by this particular neuron. For each component, the distribution of weights is strongly unimodal, centred at zero (Figure  7a ), and rather symmetric (although it is skewed to one side for some components).
In other words, there are no distinct sub-populations of neurons predominantly expressing a particular component; rather, each individual neuron can be visualized as a random linear combination of these components. We confirmed this observation by applying a recently developed clustering algorithm  (Rodriguez and Laio, 2014)  to the population of neurons in the 15-dimensional space of dPC weights. In all cases, the algorithm found only one cluster (Figure  7b ). An alternative clustering analysis with Gaussian mixture models yielded similar results (data not shown). This absence of any detectable clusters of neurons has been noted before  (Machens et al. , 2010)  and was recently observed in other datasets as well  (Raposo et al. , 2014).
Discussion Mixed selectivity of neurons in higher cortical areas has been increasingly recognized as a problem for the analysis of neurophysiological recordings, with many different approaches suggested to deal with it  (Brody et al. , 2003; Machens et al. , 2010; Machens, 2010; Brendel et al. , 2011; Rigotti et al. , 2013; Pagan and Rust, 2014; Park et al. , 2014; Raposo et al. , 2014; Cunningham and Yu, 2014). The main strength and the main novelty of the method suggested here (dPCA) is that it offers a unified and principled way of analyzing such data. Demixed PCA combines the strengths of existing supervised and unsupervised approaches to neural population data analysis (Table  1 , see also the first section of the Results). Supervised   (Rodriguez and Laio, 2014). For each dataset we took the first 15 dPCA components, and then ran the clustering algorithm in the 15-dimensional space of encoding weights.
DOI: 10. 7554/eLife. 10989. 010 methods can characterize population tuning to various parameters of interest but often do not faithfully represent the whole dataset. Unsupervised methods can capture the overall variance but are not informed by task parameters. Our method yields components that capture almost as much variance as PCA does, but are demixed. We view both properties as equally important. On one hand, demixing can greatly simplify visualization and interpretation of neural population data. Indeed, in all cases presented here, all the major aspects of the population activity that had previously been reported are directly visible on the dPCA summary figure. On the other hand, faithful representation of the population activity (i. e. 'capturing variance') avoids that a particular interpretation distorts characteristic features of the data.