dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). All Robust04 and GOV2 topics and relevance judgements are used as a held-out test set. Table  15  shows the results of our Sim-Pair BERT + BM25 model compared to the Birch (MS) baseline. The results clearly indicate that our model outperforms Birch (MS). Since our model already outperforms the baseline with a BERT Base version, it unnecessary to conduct the same experiment with a BERT Large whose computational cost is, unfortunately, beyond our hardware limitation 6. BERT-MaxP (MS). The configuration of this baseline is the same we used in the multi-phase fine-tuning setting. We compare the results of Sim-Pair BERT fine-tuned first on MS MARCO and then further fine-tuned on the target task obtained with a 5-fold cross validation with BERT-MaxP (MS) in Table  16. We report the results when using the exact match marking during fine-tuning on MS MARCO passages only  [MS] , and the results with the full marking on both MS MARCO and in-domain data  [MS+ID].
Third, when adding in-domain fine-tuning on top of the first generalpurpose fine-tuning phase on out-of-domain data, we demonstrated through an ablation study that using exact match marking in the general-purpose finetuning phase on large out-of-domain data is enough to achieve substantial leaps in performance especially on descriptions. We publish our fine-tuned checkpoints on MS MARCO so it can be accessible to the community as a more effective alternative to a vanilla checkpoint. Fourth, we study the contribution of our exact match marking strategy on a BERT variant, ELECTRA, that has been recently used in state of the art models such as Parade  (Li et al. , 2020). Experiments showed that exact match marking is indeed beneficial for ELECTRA, especially in the zero-shot transfer setting where no in-domain annotated data is used for training.
25 The participants in the AS arm had moderate-to-severe atrophic AS on their faces (regardless of the location and severity of their past active acne), as per the self-assessment of clinical acne-related scars (SCARS) definitions,  26  and no active acne on the face for at least 2 years prior to the study. Participants for the FTA study arm (Table  1 ) were aged between 13 and 40 years with moderate-to-severe active acne on their face, neck, shoulders, chest/torso, and/or back. Eligible participants for the AS study arm (Table  1 ) were aged between 18 and 45 years. A total of 60 individuals, 30 with active FTA and 30 with AS (5 in each country and each arm) were recruited. Four of these 60 participants did not complete the personification exercise. Data from the remaining 56 participants were included.
27, 28 The personification process of the condition disassociates the respondents to the answer they give, deactivating their conscious defenses. As such, this projective exercise gives permission to participants to challenge their condition, ask for accountability, and express their disagreement with their condition. [31]  Thematic analysis was performed using NVivo, version 12 Plus (QSR International) to code and categorize the data. The first letters of the FTA study were initially coded by 2 researchers who then reconciled any discrepancies into a final codebook. The codebook was applied to all the letters, allowing comparison of the FTA and AS data sets (Table  2 ). The analytic approach used was inductive, starting from the code, that is the smallest descriptive analytic unit to the theme and its interpretative stance, forming the narrative of the emotional path of the participants in the FTA and AS arms.
Peak vertical ground reaction force (F z,max ), contact time (t c ), and flight time (t f ) are key variables of running biomechanics. The gold standard method (GSM) to measure these variables is a force plate. However, a force plate is not always at hand and not very portable overground. In such situation, the vertical acceleration signal recorded by an inertial measurement unit (IMU) might be used to estimate F z,max , t c , and t f. Hence, the first purpose of this study was to propose a method that used data recorded by a single sacral-mounted IMU (IMU method: IMUM) to estimate F z,max. The second aim of this study was to estimate t c and t f using the same IMU data. The vertical acceleration threshold of an already existing IMUM was modified to detect foot-strike and toe-off events instead of effective foot-strike and toe-off events.
On the other hand, the vertical ground reaction force was estimated from the vertical acceleration signal recorded by the IMU (using Newton's second law), which allowed detecting FS and TO events using a 0 N threshold  [24, 25]. A 5 Hz low-pass Butterworth filter (8th order) was shown to result in the best correlation between t c , obtained from GSM and IMU data (sampled at 500 Hz)  [24] , while a machine learning algorithm that used data filtered using a 10 Hz 8th order low-pass Butterworth filter, resulted in an RMSE of 11 ms for t c  [25]. The vertical acceleration (sampled at 208 Hz) was also used to estimate the effective contact and flight times  [39] , two variables that allow deciphering the landing-take-off asymmetry of running  [41] [42] [43]. The authors estimated these effective timings by using a body weight threshold instead of a 0 N threshold, which allowed detecting effective FS and TO events and thus estimating effective contact and flight times.
Participant Characteristics One hundred recreational runners, which consisted of 27 females (age: 29 ± 7 years, height: 169 ± 5 cm, body mass: 61 ± 6 kg, and weekly running distance: 22 ± 16 km) and 73 males (age: 30 ± 8 years, height: 180 ± 6 cm, body mass: 71 ± 7 kg, and weekly running distance: 38 ± 24 km), were randomly selected from an existing database consisting of 115 participants  [26]  for the purpose of the present study. Participants voluntarily participated in this study, and to be included, they were required to run at least once a week and to not have current or recent lower-extremity injury (≤1 month). The study protocol was conducted according to the guidelines of the latest declaration of Helsinki and approved by the local Ethics Committee of the Vaud canton (CER-VD 2020-00334).
Experimental Procedure and Data Collection The experimental procedure and data collection has already been described elsewhere  [39]. Briefly, an IMU of 9. 4 g (Movesense sensor, Suunto, Vantaa, Finland) was firmly attached to the sacrum of participants using an elastic strap belt (Movesense, Suunto, Vantaa, Finland; see first figure in  [39] ). Then, after a warm-up run of 7-min, performed between 9 and 13 km/h on an instrumented treadmill (Arsalis T150-FMT-MED, Louvainla-Neuve, Belgium), three 1-min running trials using speeds of 9, 11, and 13 km/h were recorded in a randomized order. Three-dimensional IMU and kinetic data corresponding to the first 10 strides following the 30-s mark of the running trials were kept for data analysis. Kinetic and IMU data were not exactly synchronized. However, the synchronization delay between kinetic and IMU data was small (≤50 ms).
Therefore, kinetic and IMU data corresponded to the same 10 strides. IMU data (saturation range: ±8 g) were collected at 208 Hz (manufacturing specification) using a home-made iOS application running on an iPhone SE (Apple, Cupertino, CA, USA). The IMU orientation was such that its medio-lateral (pointing towards the right side of the IMU), posterior-anterior, and inferior-superior axes were denoted as the x-, y-, and z-axis, respectively. These IMU data were transferred to a personal computer for post processing. The force plate embedded into the treadmill together with the Vicon Nexus software (v2. 9. 3, Vicon, Oxford, UK) were used to collect kinetic data (200 Hz). In the laboratory coordinate system (LCS), medio-lateral (pointing towards the right side of the body), posterior-anterior, and inferior-superior axes were denoted as the x-, y-, and z-axis, respectively.
Inertial Measurement Unit Method The custom c++ code  [47]  used to process IMU data has already been described elsewhere  [39]. Briefly, the average angle between the z-axis of the IMU and LCS was calculated using the median values of the 3D raw acceleration data filtered using a truncated Fourier series to 0. 5 Hz in each dimension. This angle was used to align (reorient) the z-axis of the IMU with the LCS. This reorientation process was not considered in previous research that used a sacral-mounted IMU to estimate F z,max and t c  [24, 25, 31]. Then, 3D reoriented data were filtered using a truncated Fourier series to 5 Hz in each dimension. The vertical ground reaction force was approximated by the filtered vertical acceleration signal multiplied by body mass. FS and TO events were detected using a 20 N threshold, which allowed to estimate t c and t f.
These errors seemed to be comparable to those obtained using a 10 Hz low-pass cutoff frequency  [24] , though the bias and RMSE were not explicitly reported [~0. 15 BW by visual inspection of the fourth figure in  [24]  (14-19 km/h)]. In addition, the RMSE found for F z,max in the present study was equal to the RMSE obtained using two different machine learning algorithms (linear regression and quantile regression forest)  [25]. This result suggests that combining IMU data with machine learning algorithms seems to not necessarily be advantageous to estimate F z,max. Using inertial sensors placed on the legs along the tibial axis, Charry, et al. [53]  obtained a 6% error on F z,max (6-21 km/h), while Wouda, et al. [54]  achieved a 3% error (10-14 km/h) by using three IMUs (two on lower legs and one on pelvis) and two artificial neural networks.
Besides, the 20 ms RMSE obtained in our study is almost two times larger than the 10 ms RMSE reported by Alcantara, Day, Hahn and Grabowski  [25]. Such a difference might be explained by the fact that these authors predicted t c using two different machine learning algorithms (linear regression and quantile regression forest) while the present study estimates t c directly from the post-processing of the vertical acceleration signal recorded by the sacral-mounted IMU. Moreover, such a difference suggests that combining IMU data with a machine learning algorithm may improve the estimations of t c and t f compared to those obtained using IMU data alone. However, the robustness of the machine learning algorithms employed by these authors might be questioned as these algorithms were trained on 28 runners and tested on 9 runners, which is below the median value of 40 participants used for this kind of research question  [56].
INTRODUCTION Tuple-generating dependencies (TGDs) are a natural extension of description logics and referential constraints, and they are extensively used in databases. For example, they are used in data integration to capture semantic restrictions on data sources, mapping rules between data sources and the mediated schema, and constraints on the mediated schema. A fundamental computational problem in such applications is query answering under TGDs: given a query , a collection of facts  , and a set of TGDs Σ, find all the answers to  that logically follow from  and Σ. This problem has long been seen as a key component of a declarative data integration systems  [25, 33] , and it also arises in answering querying using views and accessing data sources with restrictions  [20, 26, 37]. The chase is a quintessential technique for reasoning with TGDs.
RELATED WORK Answering queries via rewriting has been extensively considered in description logics. For example, queries over ontologies in the DL-Lite family of languages can be rewritten into first-order queries  [18] , and fact entailment for SH IQ ontologies can be rewritten to disjunctive Datalog  [29]. These techniques provide the foundation for the Ontop  [17]  and KAON2  [38]  systems, respectively. In the context of TGDs, first-order rewritings were considered in data integration systems with inclusion and key dependencies  [16]. Datalog rewritings have been considered for GTGDs  [35]  and their extensions such as frontier-guarded TGDs  [11] , and nearly frontierguarded and nearly guarded TGDs  [24]. The focus in these studies was to identify complexity bounds and characterize expressivity of TGD classes rather than provide practical algorithms.
Fact Entailment for Guarded TGDs. Fact entailment for general TGDs is semidecidable, and many variants of the chase can be used to define a (possibly infinite) set of facts that is homomorphically contained in each modef of a base instance and a set of TGDs. Fact entailment is decidable for guarded TGDs (GTGDs): a TGD ∀ì  [ → ∃ì  ] is guarded if  contains an atom (called a guard) that contains all variables of ì. Note that a guard need not be unique in. Let Σ be a finite set of GTGDs. We say that a set of ground terms  is Σ-guarded by a fact ( ì ) if  ⊆ ì  ∪ consts(Σ). Moreover,  is Σ-guarded by a set of facts  if  is Σ-guarded by some fact in. Finally, a fact  ( ì ) is Σ-guarded by a fact ( ì ) (respectively a set of facts  ) if ì  is Σ-guarded by ( ì ) (respectively  ). By adapting the reasoning techniques for guarded logics  [5, 47]  and referential database constraints  [30] , fact entailment for GTGDs can be decided by a chase variant that works on tree-like structures.
Proposition 5. 12. Each application of the SkDR inference rule to rules  and  ′ as in Definition 5. 10 produces a guarded rule. Theorem 5. 13. Program SkDR(Σ) is a Datalog rewriting of a finite set of GTGDs Σ. Moreover, the rewriting can be computed in time  (   • (+  + )  ) for  the number of relations in Σ,  the maximum relation arity in Σ,  the number of existential quantifiers in Σ,   = bwidth(Σ),  = |consts(Σ)|, and some  and. It is natural to wonder whether SkDR is guaranteed to be more efficient than ExbDR. We next show that neither algorithm is generally better: there exist families of inputs on which SkDR performs exponentially more inferences than ExbDR, and vice versa. Proposition 5. 14. There exists a family {Σ  } ∈N of finite sets of GTGDs such that the number of GTGDs derived by ExbDR is  (2  ) times larger than the number of rules derived by SkDR on each Σ.
The pseudo-code is shown in Algorithm 1. The algorithm maintains two sets of TGDs/rules: the worked-off set W contain TGDs/rules that have been processed by Inf, and the unprocessed set U contains TGDs/rules that are still to be processed. Set W is initially empty (line 1), and set U is initialized to the head-normal form of Σ if Inf manipulates TGDs, or to the Skolemization of Σ if Inf manipulates rules. The algorithm then processes each  ∈ U until U becomes empty (lines 3-10). It is generally beneficial to process shorter TGDs/rules first as that improves chances of redundancy elimination. After moving  to W (line 5), Choose some  ∈ U and remove it from U 5: W = W ∪ {𝜏 } 6: Let E be the result of applying Inf to  and a subset of W and transforming the result into head-normal form 7: for each  ′ ∈ E do 8: if  ′ is not contained in W ∪ U up to redundancy then 9: Remove from W and U each  ′′ subsumed by  ′ 10: U = U ∪ {𝜏 ′ } 11: return {𝜏 ∈ W | 𝜏 is a Skolem-free Datalog rule} the algorithm applies Inf to  and W and transforms the results into head-normal form (line 6).
Program SkDR(Σ) is a Datalog rewriting of a finite set of GTGDs Σ. Moreover, the rewriting can be computed in time  (   • (+  + )  ) for  the number of relations in Σ,  the maximum relation arity in Σ,  the number of existential quantifiers in Σ,   = bwidth(Σ),  = |consts(Σ)|, and some  and.
ACKNOWLEDGMENTS This work was funded by the  EPSRC  grants  OASIS  ( EP/S032347/1 ),  AnaLOG  ( EP/P025943/1 ),  Concur  ( EP/V050869/1 ), and  QUINTON  ( EP/T022124/1 ). For the purpose of Open Access , the author has applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.
Theorem 5. 13. 513 Program SkDR(Σ) is a Datalog rewriting of a finite set of GTGDs Σ. Moreover, the rewriting can be computed in time  (   • (+  + )  ) for  the number of relations in Σ,  the maximum relation arity in Σ,  the number of existential quantifiers in Σ,   = bwidth(Σ),  = |consts(Σ)|, and some  and.
Overview This section describes the key elements of the platform implementation. We planned, designed, and implemented a web-based system to collect, store, and manage clinical data through different steps. The whole eHealth system is composed of users (patients and clinicians), a service provider (the Association La Nostra Famiglia-IRCCS Eugenio Medea, a no-profit organization providing care and rehabilitation to children with disabilities), and a service developer (SE-GE Consulting Company). The main purpose of the MedicalBIT platform is to support diagnostic flow by systematic data collection. Patients can answer questionnaires comfortably from home through a user-friendly and easily accessible interface, saving time traveling to and staying in a clinic. Clinicians have access to a real-time description of individual patients' symptoms and a graphical output of possible alerts through clinically based algorithms.
Clinicians then assign patients to different diagnostic paths for principal neurodevelopmental disorders (Attention-Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, Specific Learning Disorders, Language Disorders) and behavioral and emotional disorders based on this output. Taking into account the international gold standard, a clinical assessment is performed for each path. The final diagnostic output may differ from the initial path assignment based on the assessment results. Finally, the data collected can be easily exported in Excel (Microsoft Corporation) format for research analyses: researchers can use collected data for further analysis to develop predictive models. The platform was developed by a multidisciplinary team composed of researchers, clinicians, and informatics professionals.
They then receive a set of questionnaires according to their age and main characteristics, along with the link to the MedicalBIT assessment for completion. Users are not asked to complete all forms in one session, they can resume from where they last left. After completing all the selected questionnaires, patients are scheduled for a doctor's visit. To support the user's interaction with the platform, a user-friendly back-end interface was implemented. Through a control panel, staff can handle main functions such as administration rights, patients' lists, and support for data entry.
Design of the Output Interface A real-time graphing, panel, and table interface is available only to back-end users and contains all processed data in real time. This data is also displayed in interactive dashboards available at different access levels. Two different types of reporting are created to display the collected data (Figure  4 ): 1. Clinical output: an individual-level output, reporting patient outcome scores 2. Dashboard: group-level data, reporting summarized data in a dashboard All data stored in the database system can be exported in Excel format for further processing.
Principal Results In this study, we present the design and implementation of the MedicalBIT web platform, which allows patients to complete questionnaires comfortably from home through a user-friendly and easily accessible interface. The MedicalBIT platform, as described in this paper, allows for the timely collection of clinical data to support the diagnostic process and subsequent steps (1 and 3), and it enables the development of predictive models to improve preventive care and outcome prediction (2 and 4). A core component of the proposed model is caregivers' perspective of their child's mental health, laying the foundation for evidence-based practice and person-centered care. The MedicalBIT web platform was introduced into the clinical workflow, making patients' and caregivers' care experiences more comfortable.
As mentioned, health information technology can save time and reduce costs, thereby improving the health care experience for both patients and clinicians. For this purpose, we also collected customer satisfaction feedback. Furthermore, performance measures enable us to describe the platform with an objective measurement that allows for comparisons both within the same platform over time and between different web platforms used for similar purposes. According to reports so far, available quantitative data shows general upward trends for platform use from 2018 to 2021 despite the apparent drop during the first months of the COVID-19 pandemic, which required a complete reorganization of care provision. This trend is in line with research confirming the value of telepsychiatry  [30, 31]  not only during the pandemic period but also before and, most importantly, after the emergency.
Other reefs can host a very high fish biomass but with low biomass turnover. In this case, if these reefs are not already under some kind of protection, preserving the ecological states, processes and contributions associated with high fish biomass by placing a no-take or no-entry MPA may be appropriate when socially feasible or acceptable  24, 32. Using 1,979 tropical reef sites from a global database on fish community composition and size structure, we calculated fish biomass, biomass production and biomass turnover (Supplementary Fig. 1 ). We split reef sites into three different management classes: high standing biomass, high biomass turnover and low standing biomass/ turnover (Fig. 1 ). More precisely, we split reef sites by lower (25%) and upper (75%) quartiles for low versus high biomass turnover, and by 25% and 95% percentiles for low versus high biomass, on a logarithmic scale for both metrics.
Prediction of species growth rate We used standardized and quantitative reef fish counts from the Reef Life Survey (RLS) database  33  , among which we selected tropical and subtropical reefs (showing a minimum monthly sea surface temperature of 17 °C) spanning all tropical ocean basins (Supplementary Fig. 1 ). Following a previously published procedure, we used a broader definition of 'tropical' oceans, including locations between 17 °C and 20 °C where tropical species are present. Even though these included subtropical reefs, we hereafter refer to these reefs as tropical  34. We used an existing framework and its associated rfishprod package to estimate fish growth trajectories (K max ) using fish individual body size and sea surface temperature (the two drivers of fish growth variation across reef fish species  35  ) and mortality rates using fish individual size, maximum size and growth coefficients  36  , for the 1,400 species in our database  25.
However, this class exhibits high inter-site variability, with certain sites hosting higher fish size of carnivores, which may explain their low turnover (Fig. 2 ). This calls for detailed analyses of the trophic and size structures of these sites before considering appropriate management measures. showing the relative contributions of sea surface temperature (SST), human gravity and net primary productivity (NPP, expressed in mg C / m² / day) to the classification probability of each management class according to the random forest model using the partial function from the pdp package (v. 0. 7. 0. 9000). We excluded mid-range sites because they did not represent a management class but served as a base class. The blue lines were fitted using the locally estimated scatter-plot smoothing method of the geom_smooth function from the ggplot2 package (v.