dataesr/scientific-paragraphs-categorization huggingface dataset:
In the case of the two CPU architectures we take the total power consumption of the server as the reference measurement. In the case of GPUs the calculation is a bit more difficult. The GPUs are hosted opportunistically in the same servers used for LHCb's real-time event building (Event Building Nodes), replacing two high-speed network cards which would have otherwise been required to send data to a CPU HLT1 implemented in dedicated CPU servers. We therefore take the difference between the power consumption of the server when the GPUs are idle and when they are under load, and subtract the power consumption of the network cards which are no longer required to be present. The results are presented in Table  1  under the network replacement heading. The table also contains the calculation for a dedicated GPU machine.
I. e. instead of putting the GPUs into the Event Building Nodes, data would be sent to dedicated GPU servers which contain appropriate numbers of network cards and processing GPUs. Compared to Run 2 LHCb has gained over an order of magnitude in energy efficiency, with the gains divided rather evenly between improvements in the physics logic of HLT1, improvements in the underlying software architecture and the use of SIMD programming paradigms, and improvements from the use of newer processing architectures such as AMD's EPYC CPUs or Nvidia GPUs. The power measurements were performed using the server internal power monitoring of the baseboard management controller (BMC). The Xeon platform comes in the form factor of a quad node (4 servers sharing one power supply and cooling system). These high density servers are generally regarded as extremely power efficient.
Conclusions and implications for the future The power consumption of real-time processing architectures has traditionally been a secondary consideration compared to their physics performance and cost. However, as our experiments prepare to process ever bigger and more complex datasets in decades which will be dominated by the fight against global warming, we must expect this situation to change. To put the results of Table  1  in context, LHCb's detector and support electronics consume approximately 0. 6 MW. LHCb's warm dipole magnet has a power consumption of 4. 2 MW. Had LHCb done no work to improve the efficiency of its software, the power consumption of an HLT1 implemented on Intel's E5-2630-v4 Xeon servers (which formed the backbone of LHCb's Run 2 real-time processing) would have exceeded 1.
2 MW. If we add to this the power consumption of HLT2 as documented at the start of 2019 in  [18] , which must process an input event rate of at least 1MHz, we would have reached an overall power consumption of over 4. 8 MW, even without taking into account the overheads due to network communications, the disk servers which LHCb uses to buffer data while performing real-time alignment and calibration, and so on. Even if such a processing could be economically affordable, the relevant power consumption would be hard to defend. Additionally, LHCb's new data processing facilities (HLT1, HLT2, Event Building and data Buffering) are limited to 2. 0 MW, which would have been insufficient. Even with the gain in compute power of more modern processors we would have fallen short here. Looking further ahead, LHCb is currently proposing to build a second upgrade  [11]  in the 2030s which would roughly speaking increase the data rate and hence processing complexity by another order of magnitude.
It is then clear that without significant gains in energy efficiency, the situation will quickly become untenable. Fortunately, as we have documented, such significant gains have been achieved over the past years through a combination of software optimization, physics improvements, and exploitation of new processing architectures. The overall gain of one order of magnitude in energy efficiency is split fairly evenly between these different components, although the rethinking of our algorithms and software framework to support SIMD and SIMT processing paradigms is undoubtedly the single most important aspect contributing to this improvement. The financial savings associated with this improvement is a modest 76 kEUR per year. 4 The situation with HLT2, where much larger potential power savings are available, is complicated by the necessity to reuse LHCb's existing Run 2 servers due to financial constraints.
On one hand, optimizing for these legacy machines would give better efficiency, on the other hand it is not clear how much of these efforts can be ported over to more modern architectures. Some of the machines here are reaching 10 years of service. We expect analogous improvements and savings there on the timescale of the second LHCb upgrade, when these machines can be replaced by more modern architectures. As encouraging as these results are, a word of caution is required with regards to the future. The improvements to our software in order to exploit SIMD and SIMT processing paradigms are not something we can expect to easily repeat for the second upgrade. While further incremental improvements are certainly to be expected, gaining factors from purely software engineering improvements is unlikely.
Second, the systematic uncertainty associated to the combinatorial background model is assessed using an alternative model with no correlation between decay time and invariant mass. Finally, a systematic uncertainty associated with the model adopted for the three-body background components is determined by fitting a set of pseudoexperiments, after removing the candidates with an invariant mass below 5. 2 GeV/c 2 and ignoring the components describing this background contributions in the model. The PID efficiencies and misidentification probabilities govern the amount of crossfeed background components. A systematic uncertainty related to their calibration is determined by repeating the fit to data changing those values according to their uncertainties estimated from the calibration samples.
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MSHE (Russia) ;  MICINN (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE NP  and  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
Trial design {8} The ICAR trial is a phase III double-blind, randomized, multicenter, parallel group, concurrent, controlled study in ICU patients who required invasive mechanical ventilation (IMV) for a moderate to severe COVID-19-related ARDS. A randomization list will be prepared in advance, according to a 1:1 ratio, balanced by randomly sized blocks and stratified by center and time of IMV at randomization: less than 12 h, between 12 and 24 h, and between 24 and 96 h. It will be delivered centrally by means of a dedicated and secured Web server. One interim analysis is planned. An Independent Data Monitoring Committee (IDMC) will be responsible for closely reviewing the safety and efficacy data from the interim analysis and for providing their recommendations on the continuation of the study.
Criteria for discontinuing or modifying allocated interventions {11b} Any subject can discontinue his participation in the trial at any time for any reason. The investigator may discontinue temporarily or permanently a subject's participation in the trial for any reason concerning his safety or for his best interests. In the event of premature termination of the research, or withdrawal of consent, data collected prior to the premature termination may be used. The reasons for discontinuing participation in the research will be registered in the participant's file.
Plans for assessment and collection of outcomes {18a} Assessments will be done daily throughout the ICU stay until D28. If the patient has been discharged from the ICU, a visit will be made at D14, D21, and D28 to collect primary and secondary outcome data. An electronic case report file will be available to the research team of each institution, on an online platform (Research Electronic Data Capture (REDCap), Vanderbilt University)  [32, 33]. Activities of daily living and instrumental activities of daily living will be collected at day 90 by telephone. Study data will be collected and managed using REDCap electronic data capture tools hosted at Centre Hospitalier Sainte-Anne. REDCap is a secure, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for data integration and interoperability with external sources.
Screening and eligibility data (day 0) Baseline data (D0) Patient The following data will be recorded at the baseline visit: Weight Radiological score defined as the sum of quadrants with opacities SOFA score  [19]  and Kidney Disease: Improving Global Outcomes score (KDIGO)  [35]  CAM-ICU  [23]  IVIG adverse event occurrence  [36] : Manifestations of cutaneous hypersensitivity Occurrence of hypersensitivity manifestation with hypotension during IVIG infusion (defined as a mean blood pressure of less than 65 mmHg for 30 min, after correction for hypovolemia) Doppler ultrasound evidence of deep venous thrombosis, when performed Existence of a pulmonary embolism proven by CT scan Transfusion-associated lung injury Aseptic meningitis defined by a clinically objectified meningeal syndrome upon awakening Hemolytic anemia (defined as hemoglobin less than 8 g/dL, unmeasurable haptoglobin, and a positive direct Coombs test).
D28 and D90 follow-up Days on mechanical ventilation (considering the first 28 days after randomization) Vital status and date of death (for patients who died) Days on tracheostomy, if realized ICU complications: catheter-related infection, number of episodes of ventilator-associated pneumonia (VAP), digestive hemorrhage, pressure sores (> grade 2), confusion according to the CAM-ICU  [23] , focal neurological deficit, toxidermia Functional status: MRC score at discharge  [25] , ADL value  [21] , IADL value  [22]  Plans to promote participant retention and complete follow-up {18b} The necessary data for the assessment of the primary endpoint are systematically collected in routine care until discharge from ICU. If the patient is discharged from the hospital before D28, a telephone interview will be conducted, and the patient (or relatives) will be informed beforehand.
Confidentiality {27} During or at the end of the research, the data transmitted to the sponsor by the investigators (or any other specialized contributors) will be rendered nonidentifiable. Under no circumstances should the names or addresses of the participants be exposed. The sponsor will ensure that each person who is subject to the research has given his or her consent for access to individual data concerning him or her that are strictly necessary for the research. Plans for collection, laboratory evaluation, and storage biological specimens for genetic or molecular analysis in this trial/future use {33} Blood samples will be collected at D1, D3, D5, D7, D14, D21, and D28 for storage and further analysis for 24 patients from predefined centers. During the research, the collections will be kept in the laboratory of the GHU Paris Biological Resource Centre, for a period of 6 months, at -80 °C.
Secondary endpoints for efficacy As recommended by Yehya et al. [29] , data will also be analyzed as time to event censored at D28, within a competing risk framework, where extubation is the main event and death before extubation a competing one. Time to each event, i. e. , subdistribution hazards, will be modeled by a Fine and Gray model, with the treatment arm included as a covariate and center as a stratum. In addition, the effect size and number needed to treat (NNT) will be computed. The 28-and 90-day overall survival probability will be estimated by the Kaplan-Meier method. If the assumptions for appropriate use of the Cox proportional hazards regression model will be respected, in particular: -Independence of survival times between distinct individuals in the sample -A multiplicative relationship between the predictors and the hazard -A constant hazard ratio over time a comparison of the treatment arms will be performed with the Cox model by estimating the hazard ratio with 95% confidence interval, with treatment, participant's risk factors (age, sex, and body mass index) at baseline as the model term.
Missing data Patients discharged from the hospital before day 28 after randomization will be interviewed in order to check their vital, hospital (i. e. , new hospital, ICU admission), and respiratory status (i. e. , mechanically ventilated or not). Given the severity of COVID-19-related ARDS, it is expected that the number of patients lost to follow-up before day 28 will be minimum. In the primary analysis, for these patients, the VFD will be assumed equal to 0.
The first meeting will be held 2 months after the start of the trial and the second meeting at the end of the inclusions. Composition of the data monitoring committee, its role, and reporting structure {21a} The sponsor will ensure the safety and respect of those who have agreed to participate in the research. The sponsor will establish a quality assurance system to monitor the conduct of the research in the investigative centers. To this end, the sponsor will appoint clinical research associates and realize regular follow-up visits for each research site after the opening visits. The centralized monitoring will be performed distantly between the center's research associates and the sponsor's research assistant. Demographic characteristics, inclusion criterion, main outcome, and informed written consent collection will be systematically monitored for all participants.
The completeness of severe adverse event reports will also be monitored. A clinical research associate appointed by the sponsor will ensure the proper conduct of the research, the collection of data generated in writing, their documentation, recording, and reporting, in accordance with Good Clinical Practice as well as the legislative and regulatory provisions in force. The IDMC will be composed of Professor Raphael Porcher, Professor Antoine Roquilly, Dr. Guillaume Turc, and Dr. Franck Verdonk. They will conduct the interim analysis and provide recommendations concerning the continuation, interruption, and possible next interim analysis concerning the study.
Approximate treatment of the finite width at next-to-leading order (and its limitations) Consistent NLO QCD predictions for LHC processes involving the propagation of broad VLQs would require computations to be achieved in the complex mass scheme, as described in section 2. 2. We can however only rely on an NLO UFO model  [53]  that does not allow for complex masses. The necessary extension of such a UFO model would require the redefinition of all UV counterterms  [62]. Such an effort consists of a laborious operation, as the automated framework allowing to do so has never been tested in a new physics context featuring new coloured particles, and would be meaningful only in cases of hints of anomalies in LHC data pointing towards the existence of VLQs. We therefore consider instead an approximate treatment that involves the combination of three ingredients for a given process and a given final state (as defined in eqs.
Acknowledgements We wish to thank  G. Cacciapaglia  for discussions in the early stage of this project, and  O. Mattelaer  for his help with MG5_aMC. The authors are grateful to the  Mainz Institute for Theoretical Physics (MITP)  of the  DFG Cluster of Excellence PRISMA  + (Project  ID   39083149 ), for its hospitality and partial support during the completion of this work. AD is grateful to the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  Université de Lyon  for its financial support within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR). TF's work is supported by  IBS  under the project  code IBS-R018-D1. LP's work is supported by the  Knut and Alice Wallenberg foundation  under the  SHIFT  project, grant  KAW 2017.
0100. LP acknowledges the use of the IRIDIS 4  HPC Facility at the University of Southampton. HSS is supported by the  European Union 's  Horizon 2020 research and innovation program  under the grant agreement No. 824093 in order to contribute to the  EU Virtual Access "NLOAccess" , the  French ANR  under the grant  ANR-20-CE31-0015  (" PrecisOnium "), and the  CNRS IEA  under the grant agreement No. 205210  ("GlueGraph").
Setting up the connected device At the end of the baseline assessment, the certified exercise instructor will introduce the customised exercise programme to the participants and will give them the activity tracker and a user guide for the connected device. Then, the certified exercise instructor will explain the functioning of the activity tracker, the dedicated smartphone application and the dedicated website, as well as assist the participants to instal the application on their smartphone. The participants will be registered in the customised exercise programme by the certified exercise instructor. The registration will consist of completing a web-based questionnaire about personal and health data to determine the participant profile (age, weight, height, level of aerobic and muscular strength, treatment, symptoms, availabilities for exercise sessions and sports materials).
Sociodemographic and clinical data Sociodemographic and clinical data, including month/ year of birth, age at diagnosis of breast cancer, family status, level of education, hormonal status, tumour histology and personal history of breast cancer will be collected at baseline. Family status, potential cancer progression and all treatments received for cancer will be collected at 6 and 12 months. All data will be extracted from patients' electronic medical records, except family status and level of education that will be self-reported in a questionnaire. The occupational status will be assessed using a selfadministered questionnaire asking employment status, occupation, size of the company, the perceived intensity of the physical effort at work, the evolution of employment status at return to work in case of sick leave.
The BR-23 module gathers data about perceived body image, sexual functioning, sex enjoyment, arm symptoms, breast symptoms and systemic therapy side effects. The health-related quality of life will be assessed using the European Quality of Life-5 dimensions-5 levels (EQ-5D-5L) questionnaire. 69 This standardised selfadministered questionnaire describes five dimensions (ie, mobility, self-care, usual activities, pain/discomfort and anxiety/depression) being rated using five levels (ie, no, slight, moderate, severe and extreme problems), and comprises a 0-100 Visual Analogue Scale recording the self-rated health (where the endpoints are labelled 'The best health you can imagine' and 'The worst health you can imagine'). Fatigue will be assessed using the Piper Fatigue Scale-12 (PFS-12), a 12-item self-reported questionnaire with four subscales (ie, behavioural, affective, sensory and cognitive/mood aspects of fatigue)  70  : the higher the score, the worse the fatigue.
Medico-economic analysis The cost-effectiveness analysis will be conducted alongside the trial using the French national health insurance perspective. Quantities of resources used (external consultations, hospital stays including diagnosis-related groups, drugs with extra payments and other healthcarerelated costs) will be collected on the eCRF and multiplied by the respective unit costs. The intervention with access therapeutic patient education and the intervention with connected device will be evaluated using a bottom-up microcosting approach. 77 78 Using the diagnosis-related group, hospital stays will be evaluated based on the French National hospital costs study database. External consultations and wider examinations, community care (general practitioner visits, nurse visits, etc) will be valued on the basis of the general nomenclature of professional treatments ('Nomenclature Générale des Actes Professionnels').
DATA MANAGEMENT The database for clinical data and randomisation will be created using EnnovClinical software. Its access will be secured (personal identification and password protection) for maintaining confidentiality at all times. Individual participants will not be identified in any reports of this trial. All data from the connected device will be merged to the clinical database at the end of the study. Investigators and data analysts will have access to the final dataset. Data monitoring will be provided by the trial steering committee, including overall project supervision, progress monitoring, advice on scientific credibility and ensuring the integrity and appropriate running of the project. The clinical research assistant will verify all consent forms, compliance with established protocol and procedures, and data quality in the eCRF.
ETHICS AND DISSEMINATION The study protocol was approved by the French ethics committee (Comité de Protection des Personnes Est I, ID RCB 2017-A03360-53, 1 February 2018) and its database was reported to the French National Commission for Data Protection and Liberties (CNIL, ref. MR-001 no. 2016177, 13 December 2016). Substantial protocol modifications will be submitted to the ethics committee for approval and protocol amendment. The study findings will be widely disseminated through the clinical community by publications in international, peer-reviewed journals and by presentations at national and international conferences. They will also be communicated to patients through associations of patients' representatives and science-based information websites. They will be useful for improving the clinical care of patients with cancer and providing useful information for implementing exercise programmes for patients with cancer to health professionals, institutions and public authorities.
Acknowledgements The authors are thanksful to  Romain Buono ,  Meyssane Djebali ,  Carmen Dupuis ,  Stephany Fiore ,  Emilie Rémir ,  Cécile Dalban ,  Antonin Poullet ,  Renaud Meyrand ,  Rodolf Mongondry ,  Magali Dubois ,  Solène Poirey ,  Valentine Bertollin ,  Amélie Dupré ,  Apolline Michel ,  Axel Lion ,  Magalie Hureau  and  Morgane Sejallon  for their contribution to the conduct of the DISCO trial at the Léon Bérard cancer centre. The authors are also grateful to  Mrs Françoise Picard  from  Europa Donna France , association of breast cancer patients' representatives, for her active participation, advice and support in the DISCO trial, and to  Dr Hwayoung Noh  for English editing. Contributors BFe  (principal investigator), MT, LD and TD conceived the study. BFe and MT obtained funding for the research.