dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, we reported that feed-forward neuron networks applied to expert-based descriptors are competitive and even yield to the best performance in the raw setting (S 1 ) of proteome-wide DTI prediction. Moreover, remember that we did not fine tune this model, and most importantly, we did not considered the most relevant expert-crafted descriptors for the DTI prediction task which leaves some space for improvement and reinforces the interest of this approach. Indeed, combining multiple types of fingerprint descriptors provides the best performance  [102, 90, 103, 104]  and that the best fingerprints to retrieve a protein-ligand interaction depends on the protein target  [102, 90]. Regarding protein descriptors, proteochemometric attributes  [105]  may be more suitable for DTI prediction. Besides, neuron network-based models were not efficient on the small dataset DBEC on which shallow methods performed the best.
In particular, NRLMF appeared to be the method of choice in this case. Nevertheless, we think that most models should be evaluated on relatively large datasets, with thousands of recorded interactions, since the publicly available databases now reach this size. In particular, evaluating new methodologies on small datasets scrambled the examination of new methodologies, like in  [78]. In practical applications of DTI prediction, we finally suggest to consider an ensemble learning strategy, combining the predictions of NRLMF, FNN and the chemogenomic neuron network, since they individually perform the best on different types of the problems. Most importantly, we conclude from our observations that a promising future research direction is to integrate heterogeneous sources of data such as various bioactivity datasets, and independently, multiple molecule and protein attribute views.
CN -f eaM LP 50. 616 ± 2. 71 30. 89 ± 4. 93 42. 04 ± 2. 95 25. 77 ± 3. 60 AUPR score of various modifications of the chemogenomic neuron network on a single train/validation/test split of the DBEC dataset. Standard deviations are obtained after repeating 5 times the evaluation procedure.
03 ± 3. 8 73. 89 ± 3. 78 74. 14 ± 3. 72 53. 84 ± 8. 86 54. 46 ± 8. 43 53. 06 ± 9. 63 NRLMF 78. 8 75. 52 ± 2. 4 73. 96 ± 3. 15 56. 03 ± 9. 22 55. 91 ± 8. 07 54. 68 ± 8. 12 chemogenomic neuron network 77. 1 ± 1. 83 76. 5 ± 0. 89 76. 74 ± 0. 85 61. 9 ± 4. 41 59. 64 ± 8. 55 64. 25 ± 4. 0 73. 49 ± 3. 99 74. 04 ± 4. 91 74. 91 ± 3. 98 59. 23 ± 9. 5 58. 4 ± 8. 74 58. 62 ± 9. 1 Table 1: ROCAUC scores on the DBEC dataset in the four settings and for a test sample positive:negative ratios in {1:1, 1:2, 1:5} raw (S1) orphan proteins (S2) orphan molecules (S3) double orphan (S4) 1:1 1:2 1:5 1:1 1:2 1:5 1:1 1:2 1:5 1:1 1:2 1:5 SVM 81. 75 FNN 79. 82 ± 0. 98 68. 07 ± 2. 35 51. 55 ± 2. 55 61. 92 ± 5. 11 42. 24 ± 5. 27 24. 26 ± 3. 91 78. 57 ± 2. 22 68. 14 ± 3. 43 49. 34 ± 3. 11 60. 04 ± 8. 83 43. 55 ± 9. 84 22.
97 ± 6. 59 chemogenomic neuron network 79. 04 ± 2. 24 66. 5 ± 2. 51 49. 08 ± 4. 31 60. 79 ± 6. 24 45. 11 ± 8. 65 28. 38 ± 5. 81 76. 23 ± 2. 23 64. 61 ± 4. 78 46. 14 ± 4. 92 61. 97 ± 8. 72 45. 81 ± 8. 5 27. 0 ± 6. 96 Table 2: AUPR scores on the DBEC dataset in the four settings and for a test sample positive:negative ratios in {1:1, 1:2, 1:5} double orphan (S 4 ) raw (S 1 ) orphan proteins (S 2 ) 3 ) orphan molecules (S 1:1 1:2 1:5 1:5 1:1 1:2 1:5 1:1 1:2 1:5 1:1 1:2 49. 79 ± 1. 61 50. 25 ± 1. 84 50. 5 ± 1. 68 75. 14 ± 1. 69 SVM 74. 6 ± 0. 57 74. 6 ± 0. 55 74. 62 ± 0. 54 50. 26 ± 3. 05 49. 88 ± 3. 06 49. 88 ± 2. 95 75. 16 ± 1. 51 75. 28 ± 1. 76 50. 37 ± 1. 48 50. 29 ± 1. 55 50. 47 ± 1. 35 59. 6 ± 0. 93 NRLMF 82. 98 ± 0. 55 83. 06 ± 0. 65 83. 12 ± 0. 63 65. 18 ± 1. 36 65. 18 ± 1. 28 65. 23 ± 1.
All entities depicted in the map are assigned common identifiers converted into links to the corresponding entity descriptions in the HGNC, UniProt, Entrez, GeneCards, Reactome, and Kegg databases, and the corresponding literature references are provided (Supplementary figure  2 ). The entities on the map are annotated in NaviCell format (https://navicell. curie. fr/doc/NaviCellMapperAdminGuide. pdf).
Structural analysis of the network and model reduction For identification of the mechanisms by which PRL-3 regulates various phenotypes during invasion, we reduced the complexity of the map through a path analysis of the map with the OCSANA algorithm  42  in the BiNoM  43  plugin of Cytoscape  44. Subnetworks containing major paths and players connecting PRL-3 and the six major phenotypes of interest (cell cycle; survival; angiogenesis; adhesion; cytoskeleton remodelling; and EMT, motility and invasion), were retrieved. These phenotype-specific subnetworks of the PRL-3 map were annotated with module tags (as further explained) and used to create functional module maps accessible from the NaviCell page. The RPL-3 map thus has a hierarchical structure, and each module can be visualised in the context of the global map or individually (Supplementary Figure  3 ).
Module tagging Each entity in the map can participate in one or several functional modules. The corresponding module tags are systematically assigned in the annotation of the map entities and converted into links by the NaviCell factory during the process of online map generation (Supplementary figure  2 ). The links can be used to trace the participation of entities in the different modules of the map and to facilitate shuttling between the module maps and the general map. The list of module tags corresponds to the list of functional modules and to the model diagrams in Figures  1 2 3.
Map availability and navigation The comprehensive modular map is an open-source map accessible via several web-based platforms, such as NaviCell  45  , MINERVA  46  and NDeX  47  , facilitating map navigation and curation by the community. The resource is accessible via https://navicell. curie. fr/pages/maps_PRL3. html. Each module map can be accessed separately, to facilitate use of the map. We also provide downloadable materials, such as PPI networks extracted from the map, lists of references annotating the map and module content lists in a format suitable for enrichment analysis studies.
The modular maps show how each subpart of the multifaceted invasion process is executed. Finally, the simplified diagrams represent executable minimal models, with the main players governing each phenotype during the process. The PRL-3 map is part of the Atlas of Cancer Signalling Network (ACSN, https://acsn. curie. fr)  99 , and is entitled 'The invasion and motility map'. ACSN is a web-based resource that contains a collection of maps depicting molecular processes in cancer cells and the tumor microenvironment. This tool has many applications that have been reviewed elsewhere  100. This systems biology approach to knowledge representation makes it possible to analyse -omics data in the context of the map, to understand the role of PRL-3-regulated processes in different cancer types. ACSN also forms part of the disease maps community (http://disease-maps.
Acknowledgements We thank  Nicolas Sompairac  for help with the generation of online maps with the NaviCell, MINERVA and NDex platforms. This work was supported by  COLOSYS  grant  ANR-15-CMED-0001-04 , provided by the  Agence Nationale de la Recherche  under the frame of ERACoSysMed-1, the  ERA-Net  for Systems Medicine in clinical research and medical practice, a  PRECISE   H2020 EU grant  and a  Retina France  grant. This work received support from the  MASTODON program  from the  CNRS  (project  APLIGOOGLE ).
Dataflow languages are widely used for programming real-time embedded systems. They offer high level abstraction above hardware, and are amenable to program analysis and optimisation. This paper addresses the challenge of verifying parallel program transformations in the context of dynamic dataflow models, where the scheduling behaviour and the amount of data each actor computes may depend on values only known at runtime. We present a Linear Temporal Logic (LTL) model checking approach to verify a dataflow program transformation, using three LTL properties to identify cyclostatic actors in dynamic dataflow programs. The workflow abstracts dataflow actor code to Fiacre specifications to search for counterexamples of the LTL properties using the Tina model checker. We also present a new refactoring tool for the Orcc dataflow programming environment, which applies the parallelising transformation to cyclostatic actors.
For FPGAs, dataflow programming environments represent a high level abstraction above Hardware Description Languages (HDLs) i. e Verilog or VHDL. The dataflow programming model can be exploited for high FPGA performance, e. g. [1]  , or as an Intermediate Representation in compilers for higher level FPGA languages e. g. [2]. The dataflow abstraction enables program analysis e. g. optimal static scheduling  [3]  and program transformation (this paper).
This complicates parallelisation, due to the possibility of changing the read/write order on mutable variables, or introducing race conditions. Moreover, Hume lacks tooling for programmers to apply program transformations. In contrast, our work uses model checking to verify transformations of stateful actors, and we have extended the Eclipse based Orcc IDE with a graphical tool to apply a parallel transformation when model checking enables it. The most salient related work in the context of parallelising dataflow programs is the StreamIt compiler. That approach is restricted to parallelising stateless actors only  [11]  , whereas our use of model checking enables the parallelisation of stateful actors too. Related work that combines dataflow models with model checking includes determining minimum dataflow buffer sizes  [12]  , and enabling compile-time scheduling of multirate static actors  [13].
Our approach and contributions Approach. Our approach ( Fig. 1  ) extends dataflow parallelisation beyond stateless actors, to actors that are stateful and contain firing rules with different data rates, provided they have cyclo-static properties. This is important because in practise cyclo-static actors very often hold state between action firings ( Section 7. 2 ). This is achieved by using model checking to identify parallelisable multi-rate static (MRDF) and cyclostatic (CSDF) actors that coexist with dynamic (DDF) actors in dataflow programs. These dataflow models are described in Section 2. 2. The approach is general to any dynamic dataflow language, this paper demonstrates the approach with the CAL dataflow language  [15]  in the Orcc development environment  [16]. It marks cyclostatic actors as parallelisable, to enable an interactive graphical program transformation tool.
Properties of dataflow languages Dataflow programs are directed graphs of connected actors. There are many dataflow languages for multicore processors and embedded systems, each exhibiting a trade-off between expressivity and reasoning power. The hardware architecture they are designed to target reflects their functionality, specifically the graph structure, scheduling and data rates of programs expressed with them, shown in Fig. 2. Graph structure. Some languages support dynamic task graphs, i. e. where the number of actors change during runtime. An example is Cilk  [17]  , a C ++ fork/join model that creates parallel threads with spawn and synchronises their results with sync. This is similar to OpenMP's task thread creation and taskwait synchronisation pragmas  [18]. With other languages, the number of tasks (or actors) and the task graph topology is static at compile time, and does not change at runtime.
g. Quartz  [26]  , where all enabled actions execute in a single firing. Some dataflow programming environments only support static dataflow models, e. g. direct feedthrough function blocks in Simulink  [27]. The open source Orcc development environment  [16]  supports dynamic dataflow programming with CAL  [15]. Real world application of dynamic dataflow properties include realtime Twitter data processing  [28]  and MPEG decoding, a widely used dataflow benchmark with practical relevance. [29]  presents an MPEG implementation in CAL. The disadvantage of dynamic dataflow is that it inhibits program analysis, meaning a programmer has to revert to manual program refactoring to improve performance, and although this can improve FPGA performance  [30]  , this approach is cumbersome and error-prone.
Abstract dataflow transition system for CAL An abstract transition machine model for describing dataflow actors  [32]  is now presented to formalise the actor model. It describes the data rates and scheduling rules for actors in terms of transitions on an abstract machine. The firing of actions is determined by an actor's FSM, a labeled transition system between control states. An actor is a sequential process, communicating values by transitioning between states in the actor's FSM. Executions of statements in the body of an action can compute output token values and can update the store. The state machine receives tokens and reacts to them, possibly entering another state, and possibly producing tokens. The dataflow transition: 𝜎⟨𝑎𝑐 𝑡𝑖𝑜𝑛 1 ← 𝑒 ⟩ 𝑠 ↦𝑠 ′ ⟶ 𝜎 ′ ⟨𝑎𝑐𝑡𝑖𝑜𝑛 2 ← 𝑒 ′ ⟩ (fire) describes a transition from FSM control state  to ′ by firing action 1 , and at ′ the enabled action is action 2.
Fiacre We abstract CAL code to Fiacre  [33]  to abstract actor to a model for checking cyclostatic properties. Fiacre is a formal intermediate model to represent both the behavioural and timing aspects of embedded and Fig. 6. Automota for Fiacre Process P. distributed systems for formal verification. It is a target language of program modelling tools for use with verification engines. From Fiacre specifications, the Fiacre compiler produces enriched Petri nets, in which transitions can test and modify a set of data variables. Therefore Fiacre provides a high level, compositional syntax for Time Petri Nets. TINA  [34]  is a model checking toolset to analyse these nets.
Cyclo-Static actors as LTL formula Once the mapping abstracts Fiacre process descriptions from actors, the Fiacre compiler translates them to enriched Petri nets (  [37]  ) for the purpose of LTL verification by TINA to search for counterexamples of cyclostatic properties. LTL (Linear Temporal Logic) is a temporal logic in which a formula can encode time (logical) and future state observations along runs, where each state in time has a single successor.
Cyclo-Static periodic data rate Finally, we check for the same static data rate for every loop through the verified periodic cycle with: The LTL static_production_rate property reads: at each state along each run, either P_static is true or it eventually becomes continuously true. A static consumption rate is checked with the static_consumption_rate property, which tests for the same temporal observations for cyclic consumption rates. Whilst a simple mechanism, the P_static and C_static booleans enable the model checker to search for a single action firing sequence counterexample whereby the P and C data rate counters deviate from a fixed value after an initial periodic cycle. Should a counter example not exist, i. e. an actor is cyclo static, these two values are used by the parallelisation algorithm in Section 5.
Programming environment We have extended the open source Orcc dataflow programming environment (  [16]  ) with a refactoring tool, shown in Fig. 10  , for parallelising static and cyclo-static actors which introduces fork and join actors to scatter/gather data. The user chooses the parallelism degree, i. e. how many actors to take the place of the previous single actor. Orcc has multiple backends for different target architectures. We have added two additional backends to support the tool: 1) CAL, to generate new actor code for the fork and join actors and 2) Fiacre, for model checking actors. Fig. 11  shows the graphical consequence of applying the transformations.
Machine checked verification would be the only feasible option to overcome this, e. g. with the model checking approach that this paper presents. Automating the verification and parallelisation approach for the full CAL language will require some additional engineering: (1) the implementation of a parser for the TINA model checking output, (2) an extension of the verification approach to support multiple input or output ports, and (3) a CAL language extension for explicit cyclic sequences to be expressed ( Section 4. 2. 2 ). Supporting multiple actor ports (task 2) will likely be the most trivial, because there is a direct mapping from CAL ports to Fiacre ports ( Section 3. 3. 1 ). Extending the syntax of CAL FSMs to support a programmer's claim of a deterministic cyclic sequence through states (task 3), for model checking to verify, will require an extension to Orcc's frontend i.
Acknowledgements We acknowledge the support of the  Engineering and Physical Research Council  grant references  EP/K009931/1  (Programmable embedded platforms for remote and compute intensive image processing applications),  EP/N014758/1  ( The Integration and Interaction of Multiple Mathematical Reasoning Processes ) and  EP/N028201/1  ( Border Patrol: Improving Smart Device Security through Type-Aware Systems Design ), and the  Scottish Funding Council  for a SICSA Postdoctoral and  Early Career Researcher Exchanges grant.
By detecting signals from the scattered light of laser shots, we evaluated the performance of EUSO-SPB1  [9]. Figure  1  shows the EUSO-SPB1 track on the Google Earth image. Positions as of 12:00 UTC are labeled with the day of the month. EUSO-SPB1 resided around the Anti-meridian. This time corresponding to around the local midnight (hereafter nights mean the local nights of UTC dates). Figure  2  displays the variation of EUSO-SPB1 flight level. Bright regions indicate hours in which any data acquisition (DAQ) was made by the EUSO-SPB1 telescope. EUSO-SPB1 onboard NASA's Super Pressure Balloon (SPB) was released from the Wanaka airport, New Zealand on April 24th, 2017 at 22:50 UTC, around the noon of April 25th local time. EUSO-SPB1 first reached the nominal flight level of ∼33 km in 2 hours.
We employed ECMWF (European Centre for Medium-range Weather Forecasts) data  [19]  for such inputs. CTH retrieval was based on the cloud fraction which expresses the cloudiness in a model grid-box. Outputs are given in 10-minute intervals for gridboxes of a ∼3 km pixel over 49 pressure levels below ∼ 30 km height. We defined CTH by the highest grid-box with a cloud fraction greater than 0. 2 on horizontal pixel. Figure  4  displays a WRF output for 12:00 UTC of April 29th. The left panel visualizes gridboxes with a non-zero cloud fraction seen from southwest. The middle and right show the CTH map and distribution in a ∼300 km×300 km area around the EUSO-SPB1 position, respectively. Figure  5  displays the CTH variation from April 24th through May 2nd. Bright regions denote the night time between twilights at the EUSO-SPB1 position.
The WRF results show that the CTH variation was moderately small and was as low as a few kilometers until April 29th. In the following nights, CTH reach the height of tropopause. The CTH variation are used for studying the cloud impact on the air shower detection with the simulations. So far, we have estimated how many air shower events could be detected during the EUSO-SPB1 flight. Using the air shower and detector simulations with 'EUSO OffLine'  [20] , it turned out to be 1. 2±0. 4 event for 21. 7 h assuming the clear atmospheres  [9]. These simulations take into account the calibration of the EUSO-SPB1 telescope in the laboratory. The peak energy of the triggered events has been found consistent with the laser detection results in the field test. In the present work, we investigate the role of the cloud in the air shower detection using the ESAF (EUSO Simulation and Analysis Framework) software  [21].
Acknowledgments This work was partially supported by  NASA  grants  NNX13AH54G ,  NNX13AH52G ,  French Space Agency (CNES) ,  Italian Space Agency  through the  ASI INFN  agreement n. 2017-8-H. 0 , Italian Ministry of Foreign Affairs and International Cooperation , the  Basic Science Interdisciplinary Research Projects of RIKEN  and  JSPS KAKENHI  Grant ( 22340063 ,  23340081 , and  24244042 ),  Deutsches Zentrum für Luft und Raumfahrt , and ' Helmholtz Alliance for Astroparticle Physics HAP ' funded by the  Initiative and Networking Fund of Helmholtz Association (Germany). We acknowledge  NASA Balloon Program Office, Columbia Scientific Balloon Facility, Telescope Array Collaboration , and Wanaka airport. The present research used resources of the  National Energy Research Scientific Computing Center (NERSC) , a  US Department of Energy Office of Science User Facility  operated under Contract No.
Implementation Our method is implemented as a plugin to pygtftk  (Lopez et al. , 2019)  and can be passed a GTF/BED stream or file (examples in documentation and Supplementary Data). Most of the code is written in Python 3, with performance-critical operations written in C++ and/or Cython  (Behnel et al. , 2011). To preserve RAM, the total number of shuffles to be computed is divided into batches. The tool will compute the overlap between the supplied BED region file and (i) any desired GTF feature, or (ii) features derived from GTF file attributes (e. g "gene_biotype"), or (iii) additional regions supplied as BEDs. It will output overlap statistics and the associated p-values. The computing cost scales with the total number of lines in the reference and query files. A typical pairwise enrichment analysis of 10k regions against 10k takes 62 seconds on an 2,5 GHz Intel Core i7 processor.
Regarding subjective well-being, Stephens-Davidowitz and Varian  [22]  used Google data to study trends of depression and anxiety. Stephens-Davidowitz  [23]  used Google Trends data to examine the role of racism in the 2008 and 2012 presidential elections. Closer to us, Schwartz et. al. [24]  developed techniques that predict life satisfaction of Facebook users based on natural language. Finally, MacKerron and Mourato  [25]  use geo-spatial data to study subjective well-being in different physical environments. Large datasets collected from sources such as Google, Twitter and Facebook appeal to social scientists because they allow researchers to observe people's behavior directly rather than relying on what people say their behavior is. These data are also timely, generally available at a local level (as long as internet penetration and use is sufficient to obtain statistical representativeness), and available at low cost relative to surveys.
Following the recommendations of Monroe et al. [26] , we also combine theory and data analysis, drawing from the literature on subjective well-being, instead of applying algorithms to large datasets agnostically. Our methodology allows us to construct a model that has four important qualities: it is grounded in theory and the existing literature on well-being, it is testable and has strong out of sample performance, it is simple and transparent, and it is adaptable and can potentially be used to estimate well-being on a continuous and recurrent basis to examine the impact of shocks on well-being. We find that searches related to job search, civic engagement, and healthy habits are the most consistently important predictors of well-being across different samples. We provide two examples of the way fluctuations in estimated well-being can be used to better understand responses to events: the decline in well-being in red states following the election of Obama and the change in well-being during the months after a mass layoff announcement.