dataesr/scientific-paragraphs-categorization huggingface dataset:
e. an entity's activity) in the causal interaction, the 'Causal regulatory mechanism' reporting the biological mechanism underlying a causal interaction, and the 'Causal statement' informing about the effect of the causal interaction (e. g. upregulation or down-regulation). In addition, the MI controlled vocabulary has been extended with a 'causal interaction' branch to incorporate terminology to define regulatory terms  [19]. PSI-MITAB2. 8 is a standard format supported by the SIGNOR database, version 1. 4 of the PSICQUIC web service  [23] , and it is expected that other resources will also enable the storage and retrieval of causality in this format.
In addition, contextual annotations (e. g. evidence, experimental context and cell line) can be associated with a BEL statement thanks to the use of BEL annotations. The main advantage of a BEL statement lies in the fact that it presents a relatively simple and flexible syntax (e. g. PD-like and AF-like statements can be mixed) meaningful both to humans and computers. The Python package PyBEL has been developed to enable the parsing and validation of BEL scripts, and conversion to other formats  [34]. Several resources provide export of causal interactions in the BEL format, which are listed at: https://cthoyt. com/2020/04/30/public-bel-content. html).
The Systems Biology Markup Language for qualitative models (SBML qual), an XML-based standard format to describe regulation using the logical formalism SBML qual  [39]  is an XML-based standard developed by the Consortium for Logical Models and Tools (CoLoMoTo, http://www. colomoto. org/) and adopted by the SBML community, which is Downloaded from https://academic. oup. com/bib/article/22/4/bbaa390/6055722 by guest on 06 January 2022 part of the COMBINE initiative  [96]. It constitutes a more computational simulation-oriented format that is primarily meant to support exports of logical models. The mathematical framework encoded in this format facilitates the simulation of the annotated models. Consequently, SBML qual is suited for storing the collections of entity-based causal interactions and this provides ready-to-use models for modeling software that support this standard (e.
The different views on causal interactions Over time, scientists have adopted different visions to describe causality in causal interaction data resources, based on their biological interest and subsequent data analysis purposes (see Figure  2 ). Data resources with entity-centric description of causal interactions are predominant. In these representations, the focus is commonly placed on proteins and transcripts as actors of the causal interaction (protein-centric). The proteincentric causal statements may not report in detail gene regulatory events (see Figure  2 , Example 1. 1). In SIGNOR, for instance, the causal relation between a transcription factor and a target gene is assessed by the transcription factor (source) indirectly regulating the gene product. In a gene-centric view, the mechanistic details of gene regulation would be represented and the gene itself would be represented as the target entity (see Figure  2 , Example 1.
Notice that the description of PTMs can also vary between formats, meaning that there is no agreed consensus to represent this information. An effort to address this issue has been proposed by Danos et al. [100] , in the context of rule-based modeling. Furthermore, BEL, GO-CAM and PSI-MITAB2. 8 allow to depict with precision the biological activities of the entities involved, using GO terms. BEL in addition supports representing some of these activities using an internally defined vocabulary of a dozen high-level, shorthand terms (e. g. 'kin' for kinase activity), which PyBEL can map to GO terms (https://pybel. readthedocs. io/en/latest/_modu les/pybel/language. html). The absence of common format guidelines leads to possible data interoperability issues  [101] , as it can also be observed in Table  2  where annotations types and ontologies and controlled vocabularies used may differ.
The building of these different data resources often remains a niche activity, making it arduous for data users to build data integration protocols that are able to cope with the diversity of public data. In addition, their subsequent data analysis approaches are limited by the dataset with the least expressive value. And even if the level of annotation detail is the same, the use of a specific ontology or controlled vocabulary can vary across resources. This results in non-compatible data sets and it would demand rigorous mapping services between these sets to meet the needs of the data users. For instance, the same biological entity may be annotated and referred to different identifiers: BEL and PSI-MITAB2. 8 commonly use UniProt IDs  [90]  for proteins but BEL also enables the use of HGNC IDs  [89]  for the annotation of gene products, and whereby the context in which an identifier is used clarifies whether the identifier refers to a gene or to a specific type of gene product (e.
g. having kinase activity implies that the referred biological entity is a protein). For these reasons, data aggregation between databases can be a challenging task, or at least require additional data processing tasks. This is further compounded by the fact that comparing causal statements between resources may lead to seemingly conflicting causal interactions because of the heterogeneous contextual information (and the extent to Downloaded from https://academic. oup. com/bib/article/22/4/bbaa390/6055722 by guest on 06 January 2022 which it is supported in different formats), thereby leaving the data analyst with the task to solve possible ambiguities.
Causal interaction formats for logical modeling In logical modeling, the focus is put on entities, representing 'nodes' in a regulatory network. On the one hand, SIF, PSI-MITAB2. 8 and SBML qual lean toward an entity-based representation of causality, which makes these formats more amenable for combining causal regulatory interactions into logical statements as inputs for logical modeling. On the other hand, GO-CAM and BEL allow the representation of causality from an activity-based view for the biological entities, and a processbased view for biological reactions, meaning that the focus is not put on biological entities per se but rather on functions or actions that they can perform. In this case, the use of statements for logical modeling may require a minor post-processing of the data to extract entities' information, as logical models do not explicitly focus on activities or processes, or at least they do not represent that level of details.
e. collections of entities acting together), because this format stores a protein complex as a list of several binary interactions. These binary interactions need to be combined when building a model, in order to correctly assess the cases in which an entity is acting in combination with other entities (i. e. AND logic formulae will be added between the components of a complex). This exemplifies that different formats have different trade-offs. Managing biomolecular process information necessitates representing entities of diverse compositions, and in diverse and highly specific states (even after translating their diverse interactions into causal regulations). Tabular data (such as PSI-MITAB2. 8) are a convenient input format for data analysis algorithms, but due to the format's inherent limitations for representing this kind of information variability (as shown for protein complexes), it can still require an extra preprocessing step.
It suggests to include integrated pipelines to facilitate data reproducibility, to follow minimum requirement guidelines and use standards (i. e. MIRIAM identifiers, unique protein and gene identifiers, RDFs, SBML qual format, etc. ) for increasing data interoperability, to put effort in the development of automatic annotation tools for reducing the gaps between curation and model annotation, and to systematically use a common repository (i. e. Cell Collective  [36] , Biomodels  [37]  and the GINsim model repository  [5] ) for sharing the produced models. For the interoperable use of AF and PD graph information, conversion or mapping tools that translate between these formats are needed to seamlessly translate between any two.
Improving the sustainability of resources The domain of resources available to the logical modeler is a diverse one, ranging from readily available models to useful building blocks that are either directly available from a database or can be extracted via some intermediate software tool. Their continued availability, however, should not be taken for granted, as the existence and maintenance of these resources depends on further development and curation efforts and available grants. The fragility of this knowledge ecosystem is evident, even foundational resources such as KEGG are no exception to this, and it is there that the Elixir initiative provides for a longer term guaranteed availability of carefully curated data  [103]. While resources such as Reactome, BioModels and IntAct  [104]  (a popular and large resource of molecular interactions that is designed to become an important resource for causal molecular information) fall under the umbrella of Elixir, many others do not, and can only find long-term survival if they prepare for integration into these Elixir supported resources.
Then, we introduced software and pipelines that have been developed to infer causality either from datasets, pathway resources or a combination of both. Finally, we described data formats handling causal information from which data can be downloaded. A summary of these resources, tools and their connection is shown in Figure  3. This set of knowledge constitutes a key element to facilitate the building of logical models to better predict the behavior of a cell system. Given the range of existing resources, tools and formats, it would serve data users well if the different resources would provide thorough documentation of the curation rules followed to generate the causal statements (e. g. annotations following MI2CAST, additional contextual information, ontologies/controlled vocabularies used).
Key Points • Causal molecular interactions are key concepts supporting the assembly of regulatory networks for logical modeling. • Data resources provide a wide range of causal statements that are commonly manually curated, but tools and algorithms also support the automatic inference of causal statements from biological datasets with implicit causal information. • Several formats have been developed for the storage of causal statements, with different ranges of contextual information. • Improved interoperability between resources is dearly needed to facilitate the usability of the available data.
In this study, a numerical model of Wire Arc Additive Manufacturing has been developed to obtain the geometry of the part as well as its temperature field from the operating parameters. This predictive model takes into account electromagnetism, fluid flow and heat transfer in the arc and the melt pool. The Lorentz forces, shear stress, arc pressure, and Joule effect are calculated. This model is developed using the COMSOL Multiphysics® software. In order to simulate the addition of layer-by-layer material and the strong topological changes, the level set interface tracking method is used. This model aims to simulate the build-up of a 304 stainless steel rod starting from the operating parameters in the case of a pulsed currents. The detachment of droplets of the deposited metal and their fall along the vertical axis are modelled to predict the geometry and the thermal history of the workpiece.
This current presents a periodic signal with a peak value at a given frequency inducing the detachment of a droplet. This model requires only the knowledge of the operating parameters without any assumption on the arc distribution, to predict the temperature field in the part during the multi-layered deposition as well as its geometry. However, in order to reduce the computation time during 4 the multi-layered deposition, some simplifications are made. The numerical results are compared to experimental data giving the shape of the geometry of the solidified bead and fusion zone and as well as temperature measurements. All the numerical developments are made using COMSOL Multiphysics® software version 5. 4.
Computational domain and boundary conditions The Fig. 1  shows the geometry and the table  1  shows the corresponding boundary conditions used in the model. The shielding gas flow rate is taken into account by imposing the value of the axial velocity field along the inlet boundary at the nozzle, according to the pipe flow equation  [26] : … = 2‰ E M Š‹ G?…… P -P + ‹ G?…… P -‹ ‡ P j[ /‹ G?…… j[ ‹ G?…… /‹ ‡ OE •‹ G?…… Ž -‹ ‡ Ž + ‹ G?…… P -‹ ‡ P P j[ ‹ G?…… /‹ ‡ • ( 22 ) where ‰ E = ‰ 8 is the mass flow rate of argon shielding gas, ‰ 8 is the volume flow rate of gas, ‹ G?…… is the nozzle internal radius and ‹ ‡ is the radius of the filler wire. Since it is difficult to measure the current density distribution along AB line (Fig. 1 ), a floating potential is specified in COMSOL Multiphysics®. The software calculates a constant voltage on this boundary such that the integration of the current density equals the imposed current.
With such a boundary, the common profile of the current proposed in  [18] ,  [29]  is obtained: y = e -[ J•. H 7 ( 23 ) Radiation and convection losses are applied to the surface of the substrate. The convection losses are calculated from: { A = -ℎ A 3 -3 E' (24) where ℎ A is a convective heat-transfer coefficient and 3 E' is ambient temperature. The radiation losses are determined from: { B = -' V 3 Ž -3 E' Ž (25) where is the metal emissivity, ' V is the Stefan-Boltzmann constant and 3 is the temperature at the metal surface. For the open boundary of the plasma domain (BC line), the outflow condition of Comsol Multiphysics software is chosen, which is equivalent to an insulation condition: {. [ = 0  (26)  The materials used are argon for shielding gas and 304 stainless steel for the substrate and the consumable electrode.
Droplets From the operating parameters, it is possible to estimate the theoretical diameter of the droplets with this equation  [11] , based on mass conservation: ± ; = T6 ‡ P ‡ ; ‡ Y ; W ²/• (27) With the experimental parameter ( ‡ = 2. 45 m/min, Y ; = 25 Hz, ‡ = 0. 5 mm), the diameter of droplet is estimated at 1. 35 mm. Moreover, with the high-speed camera, it is possible to check this value. With ImageJ software and the camera, the diameter droplet is estimated to 1. 28 mm. From the model, the diameter of droplet can be estimated at 1. 26 mm (Fig. 8 ). In relation to the camera, this represents an error of 1. 5%. In order to validate the good prediction of the model, the value of frequency has been changed from 25 Hz to 12. 5 Hz. According to the equation (  7 ), the theoretical diameter becomes 1.
This paper introduces a framework of gesture recognition operating on the output of an event based camera using the computational resources of a mobile phone. We will introduce a new development around the concept of time-surfaces modified and adapted to run on the limited computational resources of a mobile platform. We also introduce a new method to remove dynamically backgrounds that makes full use of the high temporal resolution of event-based cameras. We assess the performances of the framework by operating on several dynamic scenarios in uncontrolled lighting conditions indoors and outdoors. We also introduce a new publicly available eventbased dataset for gesture recognition selected through a clinical process to allow human-machine interactions for the visually-impaired and the elderly.
Indeed, in most available data-sets such as N-MNIST and N-Caltech101  [18]  the dynamical properties of the data are artificially introduced. Even datasets such as the Poker Pips  [24]  do not contain intrinsic dynamical properties that could be used for classification. Compared to previous approaches, we emphasize the importance of using the information carried out by the timing of past events to obtain a robust low-level feature representation. Driven by brainlike asynchronous event based computations, the methodology opens new perspectives for the Internet of Things (IOT) by operating asynchronously and allocating computational resources only on active parts of the network thus achieving lesser power consumption and faster re-D r a f t sponse times. This differs from conventional image-based artificial neural network that require tremendous computational off-chip resources both for training and inference.
Authors released a dataset, DvsGesture, which we use in our experiments, obtaining comparable results while being truly event-driven in the learning and inference. This paper also goes beyond existing background suppression methodologies by using the high temporal resolution of event based cameras and thus allowing to operate on the activity of scenes rather than considering a frame based approach. This approach drastically contrasts from any existing background removal algorithm and does not rely on code-books  [9] , probabilistic approaches  [28] , sample-based methods  [3] , subspacebased techniques  [17]  or even deep learning  [2].
NavGestures-sit and NavGestures-walk Datasets The NavGestures-sit dataset was designed to operate on a smartphone using mid-air gestures. The gesture dictionary has only 6 gestures in order to be easily memorable but have also been determined as being the most elementary and sufficient set to operate a mobile phone. Four of them are "sweeping" gestures: Right, Left, Up, Down. These are designed to navigate through the items in a menu. The Home gesture, a "hello"-waving hand, can be used to go back to the main menu, or to obtain help. Lastly, the select gesture, executed only using fingers, closing them as a claw in front of the device, and then reopening them, is used to select an item. The dataset features 35 subjects, 12 being visuallyimpaired subjects, with a condition ranging from 1 to 4/5 on the WHO blindness scale and 23 being people from the laboratory.
The gestures were recorded in real use condition, with the subject sitting and holding the phone in one hand while performing the gesture with their other hand. Some of the subjects were shown video-clips of the gestures to perform, while some others had only an audio description of the gesture. This inferred some very noticeable differences in the way each subject performed the proposed gestures, in terms of hand shape, trajectory, motion and angle but also in terms of the camera pose. Each subject performed 10 repetitions of the 6 gestures. Then all the gesture clips were manually labelled and segmented. We removed clips with a wrong field of view, wrongly executed gestures or that had a device-related capturing issues. The manually curated dataset contains 1, 621 clips. The NavGestures-walk dataset contains the 6 same gestures.
DvsGesture and Faces Dataset IBM Research released a 10-class (plus a rejection class with random gestures) dataset  [1]  of hand and arm gestures, performed by 29 subjects under 3 different lighting conditions. The camera is mounted on a stand, and the subjects stand still in front of it, therefore the database is lacking dynamic backgrounds at the core of our work but provides valuable grounds for comparisons. Authors split the dataset into a train database consisting of 23 subjects and a test database consisting of the 6 remaining subjects. The Faces dataset  [13] , contains clips of the faces of 7 subjects recorded using an event-based camera. Each subject made 24 recordings, resulting in 168 clips. The subjects moved their face while following a dot on a computer screen in a square movement.
Removing the background on the NavGestures datasets If subjects are sitting in the NavGestures-sit, they do hold the phone in their hand, which results in movements and unwanted jitters that both generate background activity. In the case of the NavGestures-walk the visual background is even more present as subjects were walking while recording the dataset. Figure  4  illustrates the use of the Dynamic Background Suppression (DBS). Table  1  reports the mean percentage of events left for each gesture class after removing the background. It must be noted that we did not use the DBS on the DvsGesture dataset because it was recorded with a static camera and because the background was static, therefore there is no background to remove. The following parameters were used for the DBS: • τ b = 300µs • α = 2 • grid size : 3 × 3.
Results on the gesture datasets In our experiments on the gesture datasets (E4 to E11) we tried both 1-layer and 2-layers networks, and also the benefits of the Dynamic Background Suppression on the recognition rate. Two-layers network perform better, as they can handle changes in direction. Also the Dynamic Background Suppression greatly improves the recognition rate, as demonstrated for the NavGestures-walk, increasing the score from 81. 3% to 92. 6%. Regarding the DvsGesture dataset, we use the same 2layer network architecture. The only difference is that we increased the number of prototypes in the second layer because the gestures are more complex, and more prototypes in the end-layer account for more discriminative power.
D r a f t We also took into account the spatial component of gestures. This is possible because clips of the DvsGesture dataset all have the same framing. We split the pixel array into sub-regions, using a 3 × 3 grid. Hence, the final feature is a histogram of size 3×3×64 = 576. Classification used a nearest neighbor classifier on the histograms. One can observe in table 3 that the system performs in the same range of precision as  [1]  while being lighter to implement and compute.
Results on the Faces dataset Using a single-layer network with a receptive field R = 6, N = 32 prototypes and Tau = 5 ms, the scope is to push Figure  7 : Results obtained with visually-impaired endusers on early tests using the prototype. Users were asked to perform different scenarii such as sending a pre-written message. Average correct classification is 78%, however this score is heavily impacted by not-so-good score of the "select" gesture, which is performed in very different ways among users, something we already had reckoned during the dataset creation. the system to it limit and inquire whether a single layer is enough to capture the static properties of this dataset. We are able to obtain 96. 6% recognition score on this dataset, whereas the original model in  [13]  performed at 79% using a three-layer architecture, with its end-layer having the same number N = 32 of prototypes.
When increasing the number of prototypes to N = 48 and N = 64, we achieved respectively 97. 9% and 98. 5% in average recognition rate. Also we noticed that increasing Tau higher than 5 ms was not beneficial and decreased our classification accuracy. The data properties in this dataset are static: the dynamic does not carry any meaningful information for classifying the faces. This shows that the numerous modifications we introduced into the model lead to an important improvement in extracting the static properties. Additional material provides videos of the Dynamic Background Suppression at work and of live gesture recognition on the smartphone.
D r a f t 7 Discussion In this work, we presented a system that allows to recognize gestures using a smartphone computational capabilities. We also improved drastically the hierarchical network proposed in  [13] , both for static and dynamic data. The system and methodology allows to truly understand what the network is computing rather than the conventional black-box approach. We can report that the first layers operating on shorter time scales are extracting oriented contours and direction, while the second layers encode change of directions of the same feature. Deeper networks could theoretically encode multiple changes in direction, but given the nature of gestures and the task to be performed there no need to use such networks. We can assess that a 2-layered network is sufficient to handle efficiently any of the considered databases.