Claire Naude1
email: claire.naude@univ-eiffel.fr

Thierry Serre1

Giovanni Savino2

Cosimo Lucci2

Thomas Lich3

Impact Speed Reduction Induced By Pre-Crash Braking For Motorcycles -Parametric Evaluation Based on Real Accidents

Keywords: Pre-Crash Braking, Active Safety, Powered Two-Wheeler, Motorcycle Autonomous Emergency Braking

Motorcycle safety is a major issue, and motorcyclists have not benefited as much as motorists from advances in active safety technology. Pre-crash braking, intended to mitigate rather than avoid impact, was evaluated by simulating 60 real-world crashes from two detailed databases. A parametric study was conducted to estimate the influence of the field of view, range, triggering strategy, applied deceleration, and the presence of a visibility mask. Three representative configurations among 450 possibilities have been defined as pessimistic, average and optimistic. The median reductions in motorcyclist impact speed are respectively 0 km/h, 7.4 km/h and 11.6 km/h. The results are useful to evaluate injury and cost reduction benefits and to improve the design of detection systems as well as triggering and braking algorithms. This method, based on real accidents and pre-crash braking modeling and simulation, is reliable and relevant and can be used to evaluate other active safety systems.

Introduction

The risk of being seriously injured or killed is higher for Powered Two-Wheelers (PTW) riders than for other motorized road users. In 2019, motorcyclists accounted for 18% of all road fatalities in Europe [START_REF]Road Safety Key Figures[END_REF] whereas they represent less than 2% of the traffic. PTW is the most dangerous form of motorized transport, particularly because of the lack of protection provided by the vehicle structure, the lack of internal protection by restraint systems, and the high speeds involved. It therefore appears very interesting from a safety and economic point of view to develop safety measures for them. Yet progress in driver assistance systems is unequally distributed across modes, with lower development for two-wheelers, even though the most common types of PTW accidents could be effectively mitigated by a broader implementation of safety systems. PTW crashes often occur at intersections with a limited time to undertake an evasive maneuver. There is often inadequate braking of the PTW and problems with detection of the PTW by the other road users due to low conspicuity and high speed. Over the past five decades, passenger cars have benefited from many new safety technologies, some of which were later transferred to motorcycles -sometimes with proven success, such as the anti-lock braking system (2, 3), which has been mandatory in Europe since 2016 for new types of motorcycles 125 cc or larger, and Motorcycle Stability Control (MSC), a system which supports the rider while braking at higher lean-angle [START_REF] Lich | Motorcycle Stability Control -The next generation of motorcycle safety and riding dynamics[END_REF]. Active safety systems that would benefit PTWs include enhanced and assisted braking systems, collision warning systems, side vision assist systems, enhanced stability systems, and intersection assist systems.

Currently, one of the most advanced safety systems is Autonomous Emergency Braking (AEB). AEB is a system that uses radar and sometimes laser sensors and cameras to detect an imminent crash and activate emergency braking to decelerate the vehicle and avoid or mitigate the accident. Pre-crash braking (PCB) has similarities with AEB, but AEB is intended to avoid or mitigate impact by a target braking, while PCB is only intended to mitigate an impact by reducing the impact speed and, therefore, reduce the risk of injury during impact. The presented study was carried out within the framework of the European project PIONEERS (5), whose main objective was to improve the safety of PTWs by suggesting an integrated approach to rider protection through personal protective equipment and onboard systems. The active safety component aimed at evaluating the benefits of a PCB in terms of crash severity reduction. PCB is not a commercialized system but it has already been prototyped on some types of motorbikes and experimentally tested on track with different motorcyclists. The objective of these experiments was to verify the acceptability of the maneuvers generated by the system in case of emergency. Previous works evaluating active safety systems such as AEB have shown the significant potential benefits of such a system for cars, trucks, PTWs and pedestrians. They were mostly based on in-depth crashes reconstruction and evaluated the safety system ability to reduce impact speed [START_REF] Strandroth | Head on collisions between passenger cars and heavy good vehicles (HGV): Injury risk function and benefits of Autonomous Emergency Braking (AEB)[END_REF][START_REF] Hamdane | Issues and challenges for pedestrian active safety systems based on real world accidents[END_REF]. For PTW, simulations of AEB for motorcycles (MAEB) triggered by the system were performed, taking into account the kinematics of the accident and the dynamic capabilities of the vehicle by checking the possibilities in terms of accident avoidance or impact speed reduction [START_REF] Savino | Evaluation of an Autonomous Braking System in Real-World PTW Crashes[END_REF][START_REF] Savino | Assessing the Potential Benefits of the Motorcycle Autonomous Emergency Braking Using Detailed Crash Reconstructions[END_REF][START_REF] Savino | Further development of Motorcycle Autonomous Emergency Braking (MAEB), what can in-depth studies tell us? A multinational study[END_REF][START_REF] Savino | A robust estimation of the effects of motorcycles autonomous emergency braking (MAEB) based on in-depth crashes in Australia[END_REF]. Prototype MAEBs have been studied first for straight-line scenarios [START_REF] Savino | Exploratory field trial of motorcycle autonomous emergency braking (MAEB): Considerations on the acceptability of unexpected automatic decelerations[END_REF] and more recently in a swerve maneuver with experts (13) and common riders [START_REF] Lucci | Field testing the applicability of motorcycle autonomous emergency braking (MAEB) during pre-crash avoidance manoeuvre[END_REF]. Savino et al. [START_REF] Savino | Autonomous emergency braking for cornering motorcycle[END_REF] showed that MAEB associated with an advanced ABS (anti-lock braking system) could also be applied to cases where the PTW is inclined. To ensure a gradual introduction of MAEB and a higher acceptability among end-users, its first application may be as PCB, which would be better accepted by users since it would not be triggered untimely or too soon, when the situation is still manageable by the motorcyclist, i.e. when they can still avoid the accident by braking or swerving. This paper presents the results of the evaluation of PCB using 60 actual crashes from two highly detailed crash databases. A parametric study is presented as well as the impact speed reductions of PTWs in three realistic configurations of PCB among 450 possibilities, a "pessimistic" configuration, an "average" configuration and an "optimistic" configuration.

Material and methods

Data

The PCB evaluation was performed using computer simulations of a set of 60 real-world crashes from two highly detailed databases: the Italian InSafe database [START_REF] Piantini | Advanced accident research system based on a medical and engineering data in the metropolitan area of Florence[END_REF] and the French EDA database [START_REF] Canu | Interest of in-depth investigation to study the relation between speed and accident risk[END_REF]. 30 InSafe cases were simulated by the University of Florence (UniFI) and 30 EDA cases were simulated by the University Gustave Eiffel (Univ. Eiffel). Each accident case report includes a detailed description of the scenario and circumstances of the accident, the description of the route of the vehicles involved, the road configuration, the marks (skidding, scratching, painting...), the damage of the vehicles and interviews of witnesses and subjects involved. These reports are very complete and allow a great precision in the reconstruction of the accident. The configurations retained are collisions between a motorcycle or a scooter and a car. Loss of control of a PTW alone and accident cases where the other vehicle is coming from the side or the rear of the PTW were not considered. Both datasets show a prevalence of L3 PTW (motorcycle with an engine capacity ≥ 125 cm 3 , without sidecar) accounting for an overall 77%. In both InSafe and EDA datasets, two-thirds of the selected crashes occurred at intersections and the majority at junctions (four road segments). The main differences between the two datasets can be found for crashes that occurred on a straight road (33% in InSafe vs. 10% in EDA) and curves (no cases in InSafe vs. 7% in EDA).

PCB simulation

The simulations were performed by UniFI and Univ. Eiffel with a common methodological base already experimented by UniFI [START_REF] Lucci | Motorcycle Autonomous Emergency Braking (MAEB) employed as enhanced braking: estimating the potential for injury reduction using real-world crash modeling[END_REF] and two specific internal software tools. A set of 20 cases was simulated and analyzed by both UniFI and Univ. Eiffel to highlight their similarities and differences. The effects of PCB were assessed in terms of reduction of the absolute and relative impact speed of the PTW. First, the cases were reconstructed as accurately as possible to the real situation. The trajectories of the two vehicles involved (PTW and opponent car) and their kinematics were identified from a few seconds before the collision until a few seconds after the expected point of impact, with synchronization at impact. Then the PCB system was simulated for each case, according to the following principles (Illustrated in Figure1). 1/ Detection: The sensor is represented by a cone, which is added to the motorcycle to help determine when the other vehicle is detected. This cone is characterized by the field of view (FOV) on either side of its longitudinal axis and by the range, i.e. the maximum distance at which the opponent vehicle can be detected by the system. The definition of the cone (sensor) is based on a state-of-the-art radar technology. 2/ Triggering: The PCB system is triggered when any action of at least one of the vehicles (PTW or opponent) can no longer avoid the collision within the physical limits, with assumptions on the actual maneuverability of the vehicles involved. This "Inevitable Collision State" (ICS) algorithm was developed by UniFI in a previous study [START_REF] Savino | Inevitable Collision States for Motorcycleto-Car Collision Scenarios[END_REF]. Three PCB triggering algorithms are analyzed. On the one hand, the "standard" triggering approach deploys the PCB system as soon as the impact becomes physically unavoidable with any combination of maneuvers with constant decelerations up to 5 m/s². A so-called "progressive" approach deploys the PCB system when the collision becomes unavoidable with any combination of maneuvers with constant deceleration up to 3 m/s², thus earlier. A third approach called "conservative" deploys the PCB system with maneuvers up to 7 m/s², thus closer to collision. 3/ Activation: Once triggered, the braking maneuver is initiated at a constant level among 3 m/s², 5 m/s² and 7 m/s², after a progressive fade-in-jerk period with a jerk between 15 m/s 3 and 25 m/s 3 . The decelerations of 3 m/s² and 5 m/s² are the nominal values studied in field tests [START_REF] Marra | The future of the Autonomous Emergency Braking for Powered-Two-Wheelers: field testing end-users' acceptability in realistic riding manoeuvres[END_REF]. The deceleration of 7 m/s² was not experimentally tested and is included in the analysis to simulate the effects of PCB with new parameters that need to be studied and validated later. Furthermore, braking is only activated if in the actual accident the motorcyclist was not already braking at that moment with a higher intensity. In some cases, PCB intervention can prevent an accident altogether. This was not the primary objective of the system and it was treated as a side effect positively influencing the assessment of the benefits of PCB.

Differences between the UniFI and Univ. Eiffel simulations

According to the UniFI approach, the collision is estimated to be unavoidable when the maneuvers of the motorcyclist and the opponent motorist are no longer able to avoid the collision, with longitudinal and lateral accelerations below predefined thresholds based on the estimated maximum available grip of the studied crash case (friction ellipsoid theory). When computing the possible maneuvers, the modulus of the vectorial combination of the longitudinal and lateral accelerations does not exceed the maximum adherence available. Fixed threshold values for triggering were identified in advance using an iterative trial-and-error process, such that the median TTC at PCB triggering corresponds to the nominal values of 0.6 s, 0.8 s, and 1.0 s for the conservative, standard, and progressive activation criteria, respectively. Univ. Eiffel also used the standard, progressive and conservative triggering strategies, but with a collision avoidance algorithm using motorcyclist braking alone, with deceleration values of 5 m/s², 3 m/s² and 7 m/s². In order to obtain a more realistic behavior for detection and to take into account the results of previous UniFI studies, a constraint on the time to collision was added with realistic values of 0.6 s (conservative), 0.8 s (standard) and 1 s (progressive).

Parametric approach

A parametric study was undertaken in which the values of the PCB characteristics (field of view, range, triggering strategy, applied deceleration, speed of application...) were varying according to Table 1. The influence of the range could be studied in 40 cases, among which are 30 EDA and 10 InSafe cases. A specific study was also conducted on 30 EDA cases to estimate the influence of a visibility mask when present in the accident, such as buildings in an intersection configuration, a stopped vehicle or a wooded shoulder in a curve. In some cases, especially at intersections or in curves, with certain combinations of PCB parameters, this visibility mask prevents or delays the detection of the opponent vehicle.

Results

Parametric study

The influence of the FOV for an average set of parameters has been evaluated and results are presented in (Figure 2, left). With a FOV of 10°, detection of the opponent vehicle is zero in at least half the cases. At 25° FOV, the median impact speed reduction is 4 km/h; at 40° it is 7.5 km/h. Beyond this value, the speed reduction gain is small. All other parameters being equal, the triggering strategy induces a median reduction in impact speed of 5 km/h (conservative), 7.5 km/h (standard) and 12 km/h (progressive) (Figure 2, right)). For the 40 cases simulated by Univ. Eiffel, the influence of the range could be estimated (Figure 4

, left).

There is only a small difference of 0.2 km/h in median value between a 30 m and a 45 m range, and this difference is due to only one case. There is no difference beyond a 45 m range. The presence of a visibility mask that hinders the detection of the opponent vehicle by the PCB system was modeled for the 10 crash cases out of the 30 cases where it was present. PCB-related speed reductions with standard parameters were evaluated for the three triggering strategies, considering the 30 cases. Only the progressive strategy, with an earlier triggering, shows an influence of the presence of the obstacle to visibility, with a median speed reduction of 1.6 km/h instead of 3.1 km/h without mask (Figure 4, right).

Global influence of PCB

The five parameters taken into account for PCB and their modalities lead to 450 different configurations that allow to explore widely the different possible combinations of system settings. Nevertheless, it is difficult to get a clear picture of the expected benefits of the system. Three parameter combinations were identified to characterize typical system effects assuming a pessimistic (low efficiency), an average and an optimistic (high efficiency) configuration (Table 2). A typical radar sensor has a range of about 60 m and a field of view of +/-45°. However, the triggering of PCB is calibrated to be quite late, in order to avoid "false positive" triggers and to not intervene when the situation is catchable by the motorcyclist and/or the motorist. The parametric study showed that the field of view and the range have a limited influence on the effectiveness of the system for various crash configurations. The range was set at 30 m for the three configurations and the field of view at 25° for the pessimistic configuration and at 40° for the other two configurations. The most conservative deceleration (3 m/s²) was chosen for the pessimistic evaluation. Given the overall encouraging results of the field tests ( 21), a deceleration of 5 m/s² was chosen for the medium and optimistic parameter configurations.

. Pessimistic, average and optimistic configurations of PCB

For the 60 simulated cases, the median reductions in PTW impact speeds in the three pessimistic, average, and optimistic configurations are 0 km/h, 7.4 km/h, and 11.6 km/h (Figure 5, left). There are, however, 15 crash cases for which PCB does not activate in any of the three configurations, 4 because of the crash configuration and 11 because the rider was already braking with higher deceleration. Excluding these cases, the median speed reduction values are 2.8 km/h, 10.7 km/h and 15.1 km/h (Figure 5, right).

Discussion

The PCB evaluation was performed through numerical simulations of a set of 60 real-world crashes selected from the InSafe database provided by UniFI and the EDA database provided by Univ. Eiffel, and cinematically reconstructed. The main interest of the study is related to the overall method, which consisted in simulating real accident cases. This approach was especially enriching since the data were crossed between two different databases with accidents having occurred in two different regions. This allowed for a greater variety of situations to be analyzed, in terms of road configuration, accident scenario and speeds of the motorcyclists and motorists involved. However, there are some differences between the samples which made the comparison of the methods more difficult. The effects were evaluated in terms of PTW impact speed reduction and a parametric approach was conducted. The large number of parameters considered in the analysis (field of view, range, deceleration, fade saccade, triggering, i.e. 5x5x3x2x3 = 450 configurations) allowed for a wide exploration of the different combinations of system settings. Choices had to be made to exploit the results. In particular, the influence of each PCB parameter was studied by setting average values for the other parameters. Other crossings would be interesting as well. To achieve useful and realistic levels of speed reduction, it was decided to focus on three parameter combinations, representative of typical system effects with pessimistic (low efficiency), medium, and optimistic (high efficiency) approaches. In 8 out of 60 cases, at least one of the three configurations avoids the accident, which was not foreseen. This positively influences the benefits of PCB. In one third of the cases, the motorcyclist had braked suddenly during the accident, too late to avoid the impact. In these crashes, PCB intervention occurred only in an early activation configuration, before the motorcyclist braked. These cases reduce the overall effectiveness of PCB. The influence of the 15 accident cases where the PCB system did not activate in any of the three configurations is negative on the median reduction of impact speeds. Their inclusion reduces the values obtained from 2.8 to 3.5 km/h. This dispersion of speed reduction values led us to focus the results on the median values, which are more robust to extreme values than mean values. The results obtained for 60 cases allow general trends to be identified. To verify the representativeness and significance of the speed reduction values obtained, a much larger number of cases would have to be found, studied, reconstructed and simulated, which would be a considerable task. It should be noted that the PCB simulation method involves a rather late triggering, which reduces the influence of the consideration of visibility masks. In reality, these obstacles have a greater influence upstream and delay the detection by the motorcyclist of the opponent vehicle.

Conclusion

This study evaluated the influence of different PCB parameters on the induced PTW impact speed reduction. A FOV value of at least 25° seems required and allows a reduction in impact velocity of about 4 km/h. Going up to 40° the reduction can reach 7.5 km/h and this performance is not significantly improved for a higher FOV. The range has a limited influence on the efficiency of the system since the speed reduction gain is only 0.2 km/h between 30 m and 45 m. The three triggering strategies induce a median reduction in impact speed of 5 km/h, 7.5 km/h and 12 km/h. Taking into account the obstacles to visibility induces an increase in the impact speed of the PTW of 1.5 km/h, only in the case of an early triggering (progressive strategy). Three configurations considered realistic of a pessimistic, an average and an optimistic version of the PCB system were studied. The reductions of PTW impact speeds are 0 km/h, 7.4 km/h and 11.6 km/h. The results of the parametric study could be useful for the manufacturers of active safety systems for PTWs, for the design of the detection systems, the triggering algorithms and the automatic braking i.e., setting the deceleration level itself. The final objective is to increase the performance of safety systems such as PCB with high field robustness and rider acceptability. In the framework of the PIONEERS project, these results in terms of impact speed reduction in the pessimistic, average and optimistic configurations were directly used to evaluate injury reductions. Injury risk reductions were then converted into economic gains using the SafetyCube calculator developed in a previous project (22) to evaluate cost benefits. This study proved the feasibility, relevance and efficiency of a method based on real accident case study combined with PCB modeling and simulation. It can be used to evaluate other active safety systems already on the market or to be prototyped.

Figure 1 .

 1 Figure 1. Example of a PCB simulation for an EDA accident -key moments

  45 m, 60 m, 75 m, 90 m Field of View (FOV) +/-10°, 25°, 40°, 55°, 70° Triggering Conservative, Standard, Progressive Deceleration -3 m/s², -5 m/s², -7 m/s² Fade-in-jerk 15 m/s 3 , 25 m/s 3

Figure 2 .

 2 Figure 2. Influence of the Field Of View (Left) and the triggering strategy (Right) in PCB simulation for a set of average parametersIf we now consider the PCB deceleration variation between 3, 5 and 7 m/s², in average condition for the other parameters, the median speed reduction induced is 2.5 km/h, 7.5 km/h and 9 km/h (Figure3, left).The influence of the fade-in-jerk is quite small, with median speed reductions of 6 km/h for a fade-in-jerk of 15 m/s 3 and 7 km/h for a fade-in-jerk of 25 m/s 3 (Figure3, right).

Figure 3 .

 3 Figure 3. Influence of the deceleration (Left) and the fade-in-jerk (Right) in PCB simulation for a set of average parameters

Figure 4 .

 4 Figure 4. Influence of the range (Left) and the presence of a visibility mask (Right) in PCB simulation for a set of average parameters

Figure 5 .

 5 Figure 5. Impact speed reduction for pessimistic, average and optimistic configurations

Table 1 . PCB parameters variation

 1

t= -2.4s, no detection t = -1.7s, detection t = -0.8s, PCB braking t= -0.1s, crash with a lower speed

Acknowledgements

This study was conducted as part of the European PIONEERS project, which received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 769054. The authors would like to thank those who contributed to this work (Simone Piantini, Mirko Marra, Christophe Perrin, Adrien Canu and Thibault Le Moullac), and Marie-Sophie Naude for her proofreading of the English version of the article.

Impact Speed Reduction Induced By Pre-Crash Braking For Motorcycles -Parametric Evaluation Based on Real Accidents


Thomas Vuillaume
email: thomas.vuillaume@lapp.in2p3.fr

Enrique Garcia

Christian Tacke

Tamas Gal

The eOSSR library

The astronomy, astroparticle and particle physics communities are brought together through the ESCAPE (European Science Cluster of Astronomy and Particle Physics ESFRI research infrastructures) project to create a cluster focused on common issues in data-driven research. Among the ESCAPE work packages, the OSSR (ESCAPE Open-source Scientific Software and Service Repository) is a curated, longterm, open-access repository that makes it possible for scientists to exchange software and services and promote open science. It has been developed on top of a Zenodo community, connected to other services. A Python library, the eOSSR, has been developed to take care of the interactivity between Zenodo, services and OSSR users, allowing an automated handling of the OSSR records. In this work, we present the eOSSR, its main functionalities and how it's been used in the ESCAPE context to ease the publication of scientific software, analysis, and datasets by researchers.

The ESCAPE OSSR

The aim of the ESCAPE OSSR (Open-source Scientific Software and Service Repository) is to provide the tools necessary for the communities to share their science products in a harmonized way respecting the FAIR principles, promoting open science and maximizing cross-fertilization by software re-use and co-development. One of the key components to achieve this goal is the software and service repository. For its concept implementation, the ESCAPE repository is using Zenodo web service through the curated escape2020 community integrated with several tools to enable a complete software life-cycle. The ESCAPE Zenodo community welcomes entries that support the software and service projects in the OSSR such as user-support documentation, tutorials, presentations, and training activities. It also encourages the archival of documents and material that disseminate and support the goals of ESCAPE. 1 Figure 1. A typical software publication workflow in the OSSR using the eOSSR library

The eOSSR library

We developed a Python library called eOSSR [START_REF] Vuillaume | eossr[END_REF] in order to allow an automated integration of Zenodo with other tools and platforms forming the OSSR as well as providing an integrated environment to external users. The library is opensource and has been published in the OSSR itself. The documentation and running examples can be found online 1 .

Metadata

Metadata: The OSSR has chosen codemeta.json as schema and format for its software metadata. The definition of this schema has been integrated within the eOSSR, thus allowing: An automated verification of the metadata. A converter between Zenodo metadata schema and the OSSR metadata

API

For other tool to communicate with the OSSR, an API was necessary. The eOSSR takes advantage of Zenodo's API to propose a set of high-level functionalities through Python functions or command-lines, such as: Requesting software in the OSSR via wide or specific searches, using plain text or recognized metadata such as keywords or file type.

The Record object is at the core of the eOSSR Zenodo API. This class handles individual Zenodo records, containing all their metadata and useful methods to manipulate them. The easiest way to retrieve a Zenodo record is from it's record_id: To communicate with Zenodo and in particular with an user restricted access, one can use zen = eossr.api.zenodo.ZenodoAPI(access_token) with its private access TOKEN provided by Zenodo2 . It will then allow to:

• zen.get_user_records(): returns all the records owned by the user

• zen.get_community_pending_requests(community): returns a list of records that have been requested to be added to a community.

• zen.accept_pending_request(community, record_id): accept a pending request into a community. The community must be owned by the token owner.

• zen.create_new_entry(): creates a new deposit Zenodo

• zen.erase_entry(entry_id): erases a deposit that has not been published yet

• zen.publish_entry(entry_id): publishes a deposit eossr-check-connection-zenodo token TOKEN project_dir DIRECTORY. : Test the connection to zenodo and all the stages of a new upload. This is particularly useful in a set of CI tests to test that the upload to Zenodo will be done without issues when making a new release of the software.

Code snippets for continuous integrations

As one of the main usage of the eOSSR is in continuous integrations to insure automated upload to Zenodo (e.g. when making a software release), we also provide snippets in the documentation than you can use directly in your CI, notably in GitLab. This fills the missing GitLab-Zenodo3 integration.

  from e o s s r . a p i . z e n o d o i m p o r t g e t _ r e c o r d r e c o r d _ i d = 6826881 r e c o r d = g e t _ r e c o r d ( r e c o r d _ i d )Here a some of the most useful methods associated to the record object:• record.print_info(): prints general information (title, version, description, URL...) about the record to a stream 1 https://escape2020.pages.in2p3.fr/wp3/eossr/• record.metadata: all records metadata• record.get_associated_versions(): retrieve all associated versions of that record published in Zenodo• record.get_codemeta(): directly reads the content of the codemeta.json file at the root of a record record.write_zenodo(): writes the record metadata to a .zenodo.json file

  A set of command lines are also provided to ease the use of the eOSSR: eossr-codemeta2zenodo codemeta.json. : Converts a metadata descriptive files from the the CodeMeta to the Zenodo schema. Creates a .zenodo.json file from a codemeta.json file.eossr-upload-repository token TOKEN input-dir DIRECTORY. : Uploads a directory to the OSSR as record. The directory must include a valid zenodo or codemeta file to be used as metadata source for the upload. If not record_id is passed, a new record is created. Otherwise, a new version of the existing record is created.

	• eossr.api.zenodo.zenodo.search_licenses(): general search function
	for known licenses
	Specific OSSR functions are then built on this:
	• eossr.api.ossr.get_ossr_pending_requests()
	• eossr.api.ossr.get_ossr_records()
	2.3. Command line interface
	The API also provides several general purpose functions to query Zenodo based on a
	text search:
	• eossr.api.zenodo.zenodo.search_records(): general search function for
	records
	• eossr.api.zenodo.zenodo.search_communities(): general search func-
	tion for communities
	• eossr.api.zenodo.zenodo.search_funders(): general search function for
	known funders
	• eossr.api.zenodo.zenodo.search_grants(): general search function for
	known grants

• zen.update_record_metadata(record_id, metadata): updates a deposit metadata

• zen.upload_dir_content(directory): packages the project root directory as a zip archive and upload it to Zenodo. If a record_id is passed, a new version of that record is created. Otherwise, a new record is created.

eossr-metadata-validator codemeta.json. : Validate a codemeta file. Raises warnings for recommended changes and errors for unvalid entries.

https://zenodo.org/account/settings/applications/tokens/new/

https://escape2020.pages.in2p3.fr/wp3/eossr/gitlab_to_zenodo.html

Acknowledgments. The ASP would like to thank the dedicated researchers who are publishing with the ASP. ESCAPE -The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement no. 824064.


Nicolas Devatine

Philippe Muller

Chloé Braud

Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding

With the growing number of information sources, the problem of media bias becomes worrying for a democratic society. This paper explores the task of predicting the political orientation of news articles, with a goal of analyzing how bias is expressed. We demonstrate that integrating rhetorical dimensions via latent structures over sub-sentential discourse units allows for large improvements, with a +7.4 points difference between the base LSTM model and its discourse-based version, and +3 points improvement over the previous BERT-based stateof-the-art model. We also argue that this gives a new relevant handle for analyzing political bias in news articles.

Introduction

Misinformation is a major threat on modern democracy, influencing political agendas in an arguably unfair way, through multiple sources that are more or less transparent in their orientations. Biased media can influence public opinion by selecting reported facts and angles, oriented presentation of events, with a proven impact, e.g. on electoral behaviours [START_REF] Dellavigna | The Fox News Effect: Media Bias and Voting*[END_REF] or public health [START_REF] Simonov | The persuasive effect of fox news: non-compliance with social distancing during the covid-19 pandemic[END_REF]. The automatic identification of such biases can thus help more transparent and democratic sharing of information, and the understanding of its typical expression.

The study of bias has generated a lot of interest in political sciences with some emphasis on its linguistics aspects [START_REF] Lee | Unreliable Sources: A Guide to Detecting Bias in News Media[END_REF][START_REF] Levasseur | Media bias[END_REF], which also gave rise to numerous studies on automating bias detection [START_REF] Hamborg | Automated identification of media bias in news articles: an interdisciplinary literature review[END_REF]. NLP approaches mostly rely on lexical information [START_REF] Recasens | Linguistic models for analyzing and detecting biased language[END_REF], or syntax [START_REF] Iyyer | Political ideology detection using recursive neural networks[END_REF], with, recently, the use of pretrained language models [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] or document-level bias distribution [START_REF] Chen | Detecting media bias in news articles using Gaussian bias distributions[END_REF].

Bias can be expressed in more subtle ways however. In the excerpts below (Figure 1), discussing the 2019 Virginia Beach mass shooting1 , we can clearly identify the difference in coverage, with specific lexical choices ("epidemic", "refuse to cover") but also different ways of presenting the event: the style is either descriptive (BBC) or more emotional (WP, Townhall) ; the writer insists on particular topics or angles (use of silencers, weapon prohibition). The choice of topics is indeed an important aspect of information manipulation [START_REF] Dietram | Framing, agenda setting, and priming: The evolution of three media effects models[END_REF], and has also generated NLP work, still lexically focused [START_REF] Card | The media frames corpus: Annotations of frames across issues[END_REF][START_REF] Baumer | Testing and comparing computational approaches for identifying the language of framing in political news[END_REF][START_REF] Field | Framing and agenda-setting in Russian news: a computational analysis of intricate political strategies[END_REF][START_REF] Morstatter | Identifying framing bias in online news[END_REF] "The Virginia Beach shooter put a sound suppressor (...) so that the death shots were muffled, perhaps denying others the warning that would have allowed them to escape. It is long past time to remove the silencer that seems to suppress action on gun-control legislation, to treat mass shooting as the epidemic it is, and do everything possible to save lives.'" (Washington Post, left-leaning) "The attack began shortly after 16:00 (20:00 GMT), at Virginia Beach Municipal Center, in an area which is home to a number of city government buildings. The area was put into lockdown by police and employees were evacuated. 'We just heard people yelling and screaming at people to get down,' Megan Banton, an administrative assistant in the building, told local television news station WAVY." (BBC, center) "The chilling fact is that mass public killers are attracted to targets where people can't defend themselves. (...) Ninetyeight percent of US mass public shootings since 1950 have occurred in places where people weren't allowed to defend themselves. But the news media refuses to cover this fact, which illustrates the need for self-defense, not for more gun control that doesn't work." (Townhall, right-leaning) In contrast, we investigate the task of predicting political orientation of news articles, while trying to consider global argumentative aspects instead of local, lexical ones. This classification task consists in predicting the political leaning of an article by considering, in our case, 3 political classes (left, center, right). Since text-level discourse analysis is still a difficult problem [START_REF] Zhang | A top-down neural architecture towards text-level parsing of discourse rhetorical structure[END_REF], our architecture encodes a document while automatically inducing latent structural dependencies as in [START_REF] Liu | Learning structured text representations[END_REF], with a focus on elementary discourse units instead of sentences. We hypothesize that structural information can help identify political sides and give insights into aspects related to the argumentative nature of different media.

We evaluate our approach on news articles [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] and also perform a preliminary interpretability study. Our contributions are: (i) a model predicting political orientation of news articles, inducing a latent structure over discourse segmented texts, with state-of-the-art results ; (ii) a preliminary analysis of the impact of lexical and structural information for bias detection. Our code is available at: https://github.com/neops9/ news_political_bias.

Related work

There are multiple ways to consider the task of classifying political ideologies, especially by varying the number and type of classes, and the level of analysis. For example, one SemEval 2019 shared task focused on identifying hyperpartisan articles [START_REF] Kiesel | SemEval-2019 task 4: Hyperpartisan news detection[END_REF]. Political bias can also be characterized by locating "propaganda techniques" in texts, as in the SemEval 2020 shared task [START_REF] Da | SemEval-2020 task 11: Detection of propaganda techniques in news articles[END_REF]. Here, we consider the task proposed by [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] based on 3 classes (left, center, right). A similar task was also considered in [START_REF] Li | Mean: Multihead entity aware attention networkfor political perspective detection in news media[END_REF], but their dataset is not available for comparison. In addition, [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] explore methods that prevent the model from using media-related information while remaining based on other lexical and syntactic ones (see section 3). They report at best 51.41% in accuracy.

Contrary to previous studies based solely on lexico-syntactic information, we hypothesize that document-level organization is crucial. Rather than relying on low-performing discourse parsers, we test Liu and Lapata (2018)'s approach: structural dependencies over sentences are induced while encoding the document. Their results indicate that the learned representations, without ever exposing the model to linguistic annotations or an external parser, achieve competitive performance on a range of tasks while arguably being meaningful. This approach is effective for summarization with the learned structures, while less complex than classical ones, capturing consistent information [START_REF] Liu | Single document summarization as tree induction[END_REF][START_REF] Isonuma | Unsupervised neural single-document summarization of reviews via learning latent discourse structure and its ranking[END_REF][START_REF] Vidhisha Balachandran | StructSum: Summarization via structured representations[END_REF]. A similar approach was shown to be effective for detecting fake/real news articles [START_REF] Karimi | Learning hierarchical discourse-level structure for fake news detection[END_REF]. While focused on discourse-level phenomena, previous studies use sentences as basic units. We experiment with a fine-grained level, discourse segments, provided by a state-of-the-art segmenter.

Model

In [START_REF] Liu | Learning structured text representations[END_REF], the sentences in each document are composed of sequences of static word embeddings that are fed to a bi-LSTM to obtain hidden representations used to compute the sentence representations, that are then passed through another bi-LSTM to compute the document representation. At both levels, representations are built using the structured attention mechanism allowing for learning sentence dependencies, constrained to form a non-projective dependency tree. Finally a 2layer perceptron predicts the distribution over class labels.

We modify the model to include the improvements proposed by [START_REF] Ferracane | Evaluating discourse in structured text representations[END_REF]. In particular: (i) we remove the document-level bi-LSTM, (ii) for the pooling operation, we aggregate over units using a weighted sum based on root scores, instead of a max pooling, (iii) we perform several additional levels of percolation to embed information from the children's children of the tree, and not only direct children.

On top of that, we skip the sentence-level structure attention as it adds an unnecessary level of composition that was found to have a negative empirical impact on the results.

Segmentation The learning of a latent structure is supposed to let the model leverage rhetorical and argumentative processes that can reflect the author's political orientation. We change the relevant textual units from sentences to more discourseoriented ones, as given by a discourse segmenter [START_REF] Muller | ToNy: Contextual embeddings for accurate multilingual discourse segmentation of full documents[END_REF]. Discourse segmentation is the first stage of discourse parsing, identifying text spans called Elementary Discourse Units (EDU) that will be linked by discourse relations.

Adversarial Adaptation Some specific cues (e.g. media name, common patterns) can reveal the media source. Since most articles from a media share the same political label, the classifier decisions are biased towards the source and models easily overfit the training set. But removing these cues is a costly, hard to generalize preprocessing step. [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] suggest two approaches: adversarial adaptation, or AA [START_REF] Ganin | Domainadversarial training of neural networks[END_REF], and triplet loss pre-training [START_REF] Schroff | Facenet: A unified embedding for face recognition and clustering[END_REF], and chose the latter based on preliminary results. On the contrary we found AA more promising: it works by adding a media classifier within the architecture whose loss will be maximized using a gradient reversal layer. The model thus learns to be discriminative for the main task while being media independent.

As the training set contains many media sources, with a long tail distribution, we only consider the 10 most frequent sources (74% of the data) for the adversarial part of the model.

Dataset and Settings

Allsides Dataset The articles are crawled from the Allsides website,2 with 192 news sources covering 109 topics. Allsides is a platform that offers an analysis of the political leaning of various Englishlanguage media at the article level by annotating them with 5 political classes that cover the whole political spectrum from the Left to the Right. The published version of the dataset3 used in [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] does not match their paper as it includes resp. 2, 817 and 119 additional articles and media. Although it complicates results comparison, we kept the published dataset which is large and seems well designed. 4 This dataset comes with two organizations: article-based or media-based. We chose the latter (30, 246 articles) where media present at training time are excluded from evaluation, which avoids evaluating the model on articles that come from media already seen during training. For complexity reasons, we removed from the training set the longest articles in terms of number and size of segments, using a threshold of 100. The final dataset contains 27, 146 articles, see Table 1. Note that the original Allsides data are divided into 5 classes, but [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] merged the two Left (resp. Right) classes.

Segmentation We kept the pre-processed data as in [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] but we experimented with both sentence-and EDU-segmented texts (see Section 3). We rely on the DISRPT2019 shared task winner [START_REF] Muller | ToNy: Contextual embeddings for accurate multilingual discourse segmentation of full documents[END_REF] text as input. 5 The model is based on the BERT pretrained transformer language model, fine-tuned for sequence tagging on plain documents from the GUM corpus [START_REF] Zeldes | The GUM corpus: Creating multilayer resources in the classroom[END_REF] not overpredict most represented classes. We observe significant differences in performance between models that use structured attention ("+SA") gaining about 7.4 points in accuracy and 6 in macro F 1 for the best version (+SA/EDU). Our full model, using GloVe, obtains higher scores that those reported in [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] (LSTM version), +8 points acc. and +6 in F 1 , and also a +3 improvement in both over their best BERT-based system. We performed a control experiment on the size of the input as [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] only consider the first 512 tokens of the articles, as this is a hard constraint on the BERT model. Reducing the input size (line 4 in Table 2) decreases model performance, showing the importance of considering the whole text and which represents an important limitation of BERT. The experiments with EDUs show the importance of having fine-grained level discourse phenomena: SA based on sentences only improves results by less than 2 points, while SA based on EDUs is much more efficient. In addition, we show the benefits of modifications made to the implementation of [START_REF] Liu | Learning structured text representations[END_REF] Regarding biases towards the topics covered, we rely on the analysis by [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] for their dataset: they showed that topics covered are fairly represented in each class and thus that it should not significantly impact the model decisions.

We also want to give here some insights into the model by an analysis with interpretablity methods at the lexical level but also with respect to the induced structure.

Saliency Map A saliency map in NLP is a method for visualizing a deep learning model by computing relative importance of each token (word) in the input based on gradients [START_REF] Tulio Ribeiro | why should i trust you?[END_REF][START_REF] Murdoch | Beyond word importance: Contextual decomposition to extract interactions from lstms[END_REF]. It allows us to identify the lexical cues that provide partial understanding of the decisions made by the model. Here, we considered the vanilla gradient approach [START_REF] Simonyan | Deep inside convolutional networks: Visualising image classification models and saliency maps[END_REF], focusing on the gradient of the loss with respect to each token embedding. From these, we can first clearly assess the positive impact of the AA method. Lexical cues used by the model without AA, such as the name of the media source, are no longer as relevant for the prediction, although still present. We notice that the model focuses on specific lexical fields depending on the political orientation of the article, such as health, numbers/statistics, economy, for left, center and right leaning articles respectively. We found that crucial information for the model are the mentions or quotes of political figures (e.g., Donald Trump, Hillary Clinton, @realDonaldTrump, Barack) by media sources of the same political side, but they also represent an important source of errors when it appears in articles of the opposite side as the model tends to use this information alone without considering its context.

It also confirms our intuition that there is relevant information in the middle and at the end of articles, even though the model usually focuses on small portions of text, and it explains why reducing the entry size results in a loss of performance. An example is provided in appendix A as heatmap.

Structured Attention Regarding structured attention, we extracted the maximum spanning trees from the attention scores using the Chu-Liu-Edmonds algorithm [START_REF] Chu | On the shortest arborescence of a directed graph[END_REF][START_REF] Edmonds | Optimum branchings[END_REF]. An example of dependency tree is given in appendix A. For a first qualitative analysis, we looked at some statistics following [START_REF] Ferracane | Evaluating discourse in structured text representations[END_REF] methodology. In particular, we measure the average height of trees (10.68), the average proportion of leaf nodes (0.77), and the average normalized arc length (0.35). Statistics per class are equivalent. The learned trees have complex (non-flat) structures which show that relevant information to the model has been encoded in them in contrast to the results obtained by [START_REF] Ferracane | Evaluating discourse in structured text representations[END_REF]. We observed that they have marked differences with "natural" structures, such as distant links and it could be interesting to add more constraints.

Conclusion

We proposed an original approach for predicting the political of newspaper based on learning a latent structure showing the importance of considering elementary discourse units over sentences to include the argumentative dimension, allowing for large improvements over past approaches. We provide preliminary qualitative results on interpreting the predictions to characterize bias. Further work will focus on relying on contextual pretrained models while overcoming limitations on document size, and improving output structures and analyses.

Acknowledgments

Nicolas Devatine's work is supported by the SLANT project (ANR-19-CE23-0022). This work was partially supported by the ANR (ANR-19-PI3A-0004) through the AI Interdisciplinary Institute, ANITI, as a part of France's "Investing for the Future -PIA3" program. This work is also partially supported by the AnDiaMO project (ANR-21-CE23-0020). Chloé Braud and Philippe Muller are part of the programme DesCartes and are also supported by the National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

Ethical considerations

We used the same data as [START_REF] Baly | We can detect your bias: Predicting the ideology of news articles[END_REF] for comparison purposes. They consist in news articles referenced by the Allsides website, which also assigns political orientation to media sources based on their expertise and some polling. 7 While the exact method is undisclosed, they allow user feedback, which is a way of validating the labels. The fact remains that political labelling is potentially subjective, evolving, and labelling the source is not the same as labelling an article from the source. We train models on that approximate information nonetheless, and it can affect the prediction performance. Also, we merged all labels from the same "side" (left/right) to have only 3 classes instead of Allsides 5 categories. The dataset is not entirely balanced between left/center/right classes, but it's not possible to tell if the distribution is representative of the whole set of potential journalistic sources.

This study is not intended to provide an accurate tool for predicting the political orientation of a news article. The prediction model is a means to analyze differences in linguistic expressions of different biases, with post-hoc analysis of the model internal representations. While revealing orientation of media sources could be a legitimate goal in itself (and is the purpose of the Allsides website), note that current models do not make reliable predictions, and their results should not be taken as such without evidence supporting their decision. This is why part of our work is to analyze and look for linguistic regularities with respect to political orientation. As existing clues are currently either shallow (lexicon) or subject to further validation (structure analysis), it does not dispense of human judgement to decide if a text if showing a bias, openly or not, towards a position on the political spectrum.

A Example Appendix

Analysis As mentioned in Section 5, it is clear from this example that there is relevant information in the middle and at the end of articles, even though the model focuses on small portions of text, which confirms the value of keeping the whole text. Political figures play an important role for the model, with entities such as "Trump" or "Mattis" (from the Right) having high scores. Furthermore, the model focuses on words or, more generally, on lexical fields that relate to the main subject of the article and that seem to be particularly sensitive for the political side considered here. President Trump ' s decision late Friday to ban transgender Americans from serving in the U.S. military was blasted by House Minority Leader Nancy Pelosi , who called the move " cowardly " and " disgusting . " The Trump administration issued a memorandum that bars people with a history of " gender dysphoria , " which would require medical treatment , from being admitted to the U.S. military " except under certain limited circumstances . " Pelosi , a San Francisco Democrat , immediately released a statement slamming the memorandum and condemning the Trump administration . " The President ' s ban is a cruel and arbitrary decision designed to humiliate transgender Americans who have stepped forward to serve our country , '' she said in a statement . " This bigoted ban weakens our military readiness and our country , and shows this president ' s stunning lack of loyalty to those who risk all to defend our freedoms . " We will continue to fight this discriminatory action , which has no place in our country . House Democrats will never allow hate and prejudice to dictate our national security . " The current policy was based on recommendations made by Defense Secretary James Mattis , who said the Pentagon found that exempting transgender people from military standards could undermine its readiness for combat . " Exempting such persons from well -established mental health , physical health , and sex -based standards , which apply to all Service members , including transgender Service members without gender dysphoria , could undermine readiness , disrupt unit cohesion , and impose an unreasonable burden on the military that is not conducive to military effectiveness and lethality , " read the recommendation that was included in a court filing . DOJ ASKS SUPREME COURT TO TAKE UP CASE OF MILITARY TRANSGENDER BAN The Trump administration asked the Supreme Court to issue an unusually quick ruling on the Pentagon 's policy of restricting military service by transgender people in a bid to bypass lower courts that previously ruled against the administration and its policy barring transgender recruits . The Pentagon initially lifted its ban on transgender troops serving openly in the military in 2016 under the orders of the Obama administration . Trump reversed the policy , prompting outrage and lawsuits , which were ruled against the Trump administration .

Figure 2: Article from "Fox News" (right-leaning) correctly predicted: "Pelosi blasts Trump's move to bar transgender troops, calls it 'disgusting' and 'cowardly'". The darker it is, the higher the relevance of the word to the model.

Figure 1 :

 1 Figure 1: Excerpts from articles on the 2019 Virgina Beach mass shooting from media with different political tendencies.

Table 1 :

 1 that only needs plain Statistics about the dataset (media-based split).

		Left	Center Right	Total
	Train	9, 618	6, 683	7, 189	23, 490
		(41%)	(28%)	(31%)
	Valid.	98	618	1, 640	2, 356
		(4%)	(26%)	(70%)
	Test	599	299	402	1, 300
		(46%)	(23%)	(31%)

  , the English dataset which has the most varied document types. We end up with an average of 49 EDUs per article, and an average of 19 words per EDU.Settings We built on[START_REF] Ferracane | Evaluating discourse in structured text representations[END_REF]'s implementation, 6 itself based on Liu and Lapata (2018)'s. We adapted the code according to the modifications and additions proposed in our approach as detailed in Section 3. Hyper-parameters were set using grid search: 200 for the hidden size of bi-LSTM and 2-layer perceptron, 0.01 for learning rate, 0.5 for dropout and 8 for batch size. We used pretrained 300D GloVe vectors. For Adversarial Adaptation, best results used a weighting factor λ = 0.7 for the adversarial part of the loss. Training is done with Adagrad optimizer, on a Nvidia GeForce GTX 1080 Ti GPU card.

	Model	Acc.	Macro F 1 MAE
	Ours Base	46.97 44.41	0.69
	Ours+SA/Sent	48.76 45.84	0.67
	Liu&Lapata+SA/EDU 51.01 48.61	0.72
	Ours+SA/EDU	54.39 51.36	0.57
	Ours: 512t, +SA/EDU 50.04 45.23	0.70
	Baly 20: 512t, LSTM 46.42 45.44	0.62
	Baly 20: 512t, BERT	51.41 48.26	0.51
	Table 2: Accuracy%, macro-F 1 %, Mean Absolute Er-
	ror (MAE, lower is better) on the test set for different
	versions of the model. "Baly 20" refers to the results
	reported in Baly et al. (2020), we did not replicate their
	experiments. "512t" means that only the first 512 tokens
	of the inputs were used to train the model. "SA" = for
	Structured Attention, and "Sent"/"EDU" is for inputs
	segmented in sentence or discourse units. We also eval-
	uate on the original model proposed by Liu and Lapata
	(2018) without the improvements added in our version
	of the model. The 95% binomial proportion confidence
	interval for the best model classification accuracy is
	2.9%
				Evaluation We evaluate four versions: (i) keep-
				ing only the bi-LSTM (Ours Base), (ii) full archi-
				tecture with structural attention and sentence seg-
				mentation (Ours+SA/Sent), (iii) full architecture
				but with EDU segmentation (Ours+SA/EDU), and
				(iv) full architecture but keeping only the first 512
				tokens of each text as in Baly et al. (2020) (Ours:
				512t, +SA/EDU). We report standard measures but
				also the mean absolute error (MAE) as this is an
				ordinal problem. We compare to scores reported in
				Baly et al. (2020) on the same split for their LSTM
				and BERT versions (limited to 512 tokens).
				5 Results and Analysis
				Results obtained by the different models are given
				in Table 2. We also report scores per class in Ta-
				ble 3 (best model) to control that the model does

https://www.allsides.com/blog/virginia-beach-shootingreinvigorates-gun-debate

http://allsides.com/

https://github.com/ramybaly/Article-Bias-Prediction

Note that the original version is not available.

Recent approaches reported improvements[START_REF] Zeldes | The DISRPT 2021 shared task on elementary discourse unit segmentation, connective detection, and relation classification[END_REF], but require more preprocessing, e.g. syntactic parses.

https://github.com/elisaF/structured/

https://www.allsides.com/media-bias/media-bias-ratingmethods

7 -"I am very grateful to the President-elect for this opportunity to serve him and this nation 8 -as we work to create an economy that works for everyone, secure our borders, repeal and replace Obamacare and destroy radical Islamic terrorism. 9 -He will be a great President for all Americans." 10 -Bannon is believed to have been in the running for the position, but will now serve as chief strategist and senior counselor. 11 -He ran the conservative website Breitbart News before joining the presidential campaign during the general election. 12 -"Steve and Reince are highly qualified leaders who worked well together on our campaign and led us to a historic victory. 13 -Now I will have them both with me in the White House 14 -as we work to make America great again", 15 -Trump said. 16 -The campaign's statement described Bannon and Priebus as "equal partners". 17 -"Bannon and Priebus will continue the effective leadership team they formed during the campaign, working as equal partners to transform the federal government, 18 -making it much more efficient, effective and productive", it said. 19 -According to CNN, Trump's picks signal that he will look to build bridges in Washington and keep continuity with the Republican party's agenda. 20 -"We will have that same partnership in working to help President-elect Trump achieve his agenda", Bannon said.