dataesr/scientific-paragraphs-categorization huggingface dataset:
However most of these analytic methods are purely symbolic and do not compute a real-time schedule, including mapping and periods, or the number of delays of the graph. Besides, many tools are able to analyze SDF graphs, to derive a few schedule characteristics (e. g. mapping and bufer size), and inally to generate the glue code of the schedule automatically: DIF-GPU  [53] , PREESM  [67] , LabVIEW  [1] , MAPS  [21] , Diplomat  [13] , Gaspard  [31] , Ocarina  [42] , PeaCE  [37] , and Ptolemy  [29]   2  which includes the Metronomy tool  [36]  for timing veriication. But these tools either do not consider real-time executions (e. g. PREESM and LabVIEW), or do not perform all the syntheses automatically (e. g. delay or periods are an input of the user). For example, periods usually are an input of the problem  [39, 45] , instead of the output.
Aine DataFlow Graph (ADFG), the name of the tool, stands for the internal representation of the UCSDF, Cyclo-Static DataFlow (CSDF), and SDF graphs. In UCSDF graphs, the actors send data through bufers as in SDF and CSDF graphs, and Deinition 3. 1 for the repetition vector is the same. The diference between these three models lies in the production and consumption rates: static in SDF (one integer), cyclo-static in CSDF (a cyclic sequence of integers), and ultimately cyclo-static in UCSDF (two sequences of integers, the irst being used only once). During its -th iring, an actor receives and sends an amount of data equal to the -th value of its consumption/production rates; the cyclic sequence is iterated in a round-robin fashion. CSDF and UCSDF graphs can always be converted into SDF graphs  [66]  but at the cost of introducing many new nodes.
In the Real-Time Executive for Multiprocessor Systems (RTEMS) operating systems the number of tasks is unlimited, but the number of priority levels is limited 8 to 255. Thus, in the ADFG tool, the code generation for the RTEMS operating systems works only on SDF graphs having at most 255 actors. Besides, µC/OS-III is not anymore limiting the number of tasks and priorities but the previous version µC/OS-II was limiting 9 the number of tasks and priorities to 255. (5). The computation of periods is based on RTA  [2] : diferent values of the periods are tested between two bounds by performing a bisection, and the smallest valid period, i. e. greater than response time, is kept. As all actor periods can be expressed relatively to the basis period basis , the algorithm is called Symbolic Response Time Analysis (SRTA).
CODE GENERATION The ADFG tool supports automated code generation of the computed bufer sizes and other schedule characteristics for datalow applications that are implemented in the Lightweight Datalow Environment (LIDE)  [52]   11  running on the RTEMS real-time operating system  12. The code generator inputs are the characteristics computed using the ADFG theory, including: bufer sizes, periods, ofsets, priorities and mapping. The generated code is then cross-compiled and tested by using the QEMU tool to emulate an ARM platform. A detailed evaluation of the code generator has been conducted and presented in  [79]. Interested readers can refer to this article for a complete presentation of our approach to integrate the ADFG tool in a framework with scheduling simulation and code generation.
Schedule characteristics in RTEMS RTEMS is an open source real-time operating system that supports open standard Application Programming Interfaces (APIs) such as POSIX. We consider the usage of RTEMS to generate the computed schedule characteristics. Actor invocations and ixed-priority periodic scheduling are achieved by the usage of the POSIX thread library. Besides bufer sizes, four schedule characteristics are computed using the ADFG theory, namely: priority, core mapping (for processor mapping), period and offset. Code generation for these characteristics is done by exploiting the POSIX pthread API supported by RTEMS. One thread is generated per actor. The priority and core mapping attributes are natively supported. The period and offset are taken into account by implementing a periodic release per actor.
• Priority: POSIX pthread_attr_setschedparam function is used to set thread priorities. • Mapping: POSIX provides the cpu_set function that allows us to choose the set of processors that a thread can execute on. • Period: periodic release of a thread is implemented by using the clock_nanosleep function with the lag TIMER_ABSTIME. It allows us to set the absolute time at which the thread shall wake up again. The wake up time is computed as below: __ := _ + (_ × ) (18) Fig. 10. Generated code configuring the schedule characteristics in RTEMS. In  [79] , we used the nanosleep function to implement periodic tasks. It is because the function clock_nanosleep was not in the list of directives provided by the clock manager in RTEMS 5. 1  13  , which is the version targeted by our code generator. However, the authors in  [11]  indicated that it is possible to use the clock_nanosleep function to implement periodic tasks in RTEMS.
EVALUATION This section presents an evaluation of the schedules synthesized by the ADFG tool. Results are compared to the state-of-the-art theoretical methods to compute bufer sizes and throughput in Section 6. 1; optimum is reached for both metrics. Scheduling simulations have also been performed to compute the number of preemptions and the usage of memory, see Section 6. 2. The code of the ADFG tool is open-source and can be accessed online  14. Experiments using Cheddar tool require its own installation; on the code repository, we provide a link to a virtual machine with ADFG and Cheddar installed. In addition, all benchmarks, scripts, and instructions to reproduce the results presented in this section are included in the code repository and in the virtual machine. Old binaries are also available online  15  , but they are outdated and the website hosting them is not maintained anymore.
Description of the platform and solvers The numerical experiments have been performed on a computing server with dual Intel Xeon Silver 4210 CPUs (in total, 20 physical cores with a base frequency of 2. 2 GHz each and 40 logical processors) with 96 GB RAM, and 4 TB solid state hard drive. The system includes a NVIDIA Tesla V100 GPU (5120 cuda cores, 7 TFlops of double-precision peak performance and 32 GB DDR5 SDRAM) with CUDA Compute Capability (CC) 7. 0 and an NVIDIA GeForce RTX 3090 GPU (5248 cores, 556 GFLOPS of double-precision peak performance and 24 GB GDDR6X) with CUDA CC 8. 6. The operating system is Linux Debian 10. 9 with GCC version 10. 2. 1 and the CUDA 11. 2 runtime. We have developed two implementations of the solver: • OpenMP solver: This solver only exploits the cores of the CPUs in the platform by using OpenMP directives and functions (see  [38]  for additional details).
In the experiments, this solver is run using 40 threads (two per physical core). To compile the OpenMP solver, we have used the GNU compiler g++ version 10. 2. 1 using the switches -fopenmp -O3 -m64 -use_fast_math. • CUDA solver: This heterogeneous code performs all the relevant computing phases on one of the available GPUs (Tesla V100 or RTX 3090) under the control of a CPU thread which invokes the corresponding CUDA kernels. In the compilation with nvcc, we have used the switches -O3 -m64 -use_ fast_math and the options necessary to generate PTX code and object code optimized to the particular GPU architecture. In the OpenMP solver, we use exactly the same numerical methods as in the CUDA solver.
TESS photometry The host star TIC 394357918 (TOI-4184) was observed by TESS mission  (Ricker et al. 2015)  in Sectors 1, 28 and 39 for 27 days each on TESS CCD 3 Camera 3. The Sector 1 campaign started on UTC July 25 2018 and ended on UTC August 22 2018. The Sector 28 campaign started on UTC July 30 2020 and ended on UTC August 26 2020. The Sector 39 campaign started on UTC 2021 May 26 and ended on UTC 2021 June 26. The star TIC 441738827 (TOI-2084) was observed by TESS in a 2 min cadence during Sectors 16 (UTC September 11 to October 07 2019), 19-23 (UTC November 27 2019 to April 16 2020), 25-26 (UTC May 13 to July 04 2020), and 48-60 (UTC January 31 2021 to January 18 2023). TOI-4184 and TOI-2084 were selected by  Stassun et al. (2018)  to be observed using the 2 min short-cadence mode. To perform TESS data modeling, we retrieved the Presearch Data Conditioning light curves (PDC-SAP;  Stumpe et al.
SPECULOOS-North We used SPECULOOS-North/Artemis to observe two transits of TOI-2084. 01. Artemis is a 1. 0-m Ritchey-Chretien telescope equipped with a thermoelectrically cooled 2K×2K Andor iKon-L BEX2-DD CCD camera with a pixel scale of 0. 35 ′′ , resulting in a field-of-view of 12 ′ × 12 ′  (Burdanov et al. 2022). It is a twin of the SPECULOOS-South (Sect. 2. 2. 1) and SAINT-EX (Sect. 2. 2. 3) telescopes. The first transit was observed on UTC 2020 August 13, and the second was observed on UTC June 25 2021. Both transits were observed in the I + z filter with an exposure time of 33 s and we performed aperture photometry in an 2 https://github. com/lgrcia/prose A38, page 2 of 20  Barkaoui, K. , et al. : A&A proofs,  Fig. 2. Phase-folded TESS transit light curves of  TOI-2081. 01 (left)  and.
We took 216 science images and performed aperture photometry in an uncontaminated aperture of 5. 6 ′′ and a PSF FWHM of 3. 7 ′′. During that second observation of TOI-2084, the telescope underwent a meridian flip at BJD 2459391. 4829. Data reduction and photometric measurements were performed using the PROSE pipeline  (Garcia et al. 2022).
Follow-up photometric validation The photometric follow-up using ground-based facilities was carried out on the basis of two objectives: identify the source of the transit event and assess if the transit depth is wavelengthdependent. The presence of contaminating stars in the TESS aperture was noted for both TOI-4184. 01 and TOI-2084. 01 in the TESS data validation reports. The closest neighboring stars are respectively TIC 650071720 at 11. 5 ′′ with a ∆Tmag of 4. 45, and TIC 441738830 at 12. 4 ′′ with a ∆Tmag of 6. 15. We reached A38, page 9 of 20 aperture sizes of a few arcseconds using ground-based facilities, which allowed us to confirm the transit events are on the expected stars for TOI-4184. 01 and TOI-2084. 01. In the case of TOI-2084. 02, at the expected transit times we detected a deep eclipse twice on the nearby star TIC 1271317080 (∆T = 4.
98) at 12. 9 ′′ from the target, labeled T3 in Fig. 10. Thus, we ruled out TOI-2084. 02 as a false positive and did not consider it further. We collected photometric data for TOI-2084. 01 in various bands (I+z, zs, i ′ , r ′ , g ′ ), spanning the 400-1100 nm wavelength ranges. We measured a matching transit depth within 1σ in all bands. Similarly, we obtained data for TOI-4184. 01 in the I+z, zs, Ic, JJ, g ′ bands, covering a range between 400 and 1210 nm where the transits depths also agree within 1σ. The transit depths measured in different wavelengths for TOI-2084 b and TOI-4184 b are presented in Fig. 11 , Tables  2  and 3.
Planet searches using the TESS photometry In this section, we search for additional planetary candidates that might have remained unnoticed by SPOC and the QLP due to their detection thresholds. To this end we used our custom pipeline SHERLOCK 8 , originally presented by Pozuelos et al. 8 SHERLOCK (Searching for Hints of Exoplanets fRom Lightcurves Of spaCe-based seeKers) code is fully available on GitHub: https: //github. com/franpoz/SHERLOCK (2020) and  Demory et al. (2020)  and used in several studies (see, e. g. ,  Wells et al. 2021; Van Grootel et al. 2021; Schanche et al. 2022). SHERLOCK allows the user to explore TESS data to recover known planets and alerted candidates and as well as to search for new periodic signals, which may hint at the existence of extra transiting planets. In short, the pipeline has six modules to (1) download and prepare the light curves from the MAST using the lightkurve (Lightkurve Collaboration 2018), (2) search for planetary candidates through the tls (Hippke & Heller 2019), (3) perform a semi-automatic vetting of the interesting signals, (4) compute a statistical validation using the TRICERATOPS  (Giacalone et al.
Results and discussion We present the validation and discovery of TOI-2084 b and TOI-4184 b by the TESS mission (see phase-folded light curves in Fig. 2  and individual transits in Fig. 3 ), which were confirmed through follow-up photometric measurements collected by SPECULOOS-South/North, SAINT-EX, TRAPPIST-South/North, MuSCAT3, LCOGT, Danish and ExTrA telescopes (see phase-folded light curves in Fig. 4 ). The host stars were characterized by combining optical spectrum obtained by Shane/Kast and Magellan/FIRE, SED, and stellar evolutionary models. We performed a global analysis of space TESS and ground-based photometric data to derive the stellar and planetary physical parameters for each system. Table  1  shows the astrometry, photometry, and spectroscopy stellar properties of TOI-2084 and TOI-4184.
Acknowledgements. This paper made use of data collected by the TESS mission and are publicly available from the  Mikulski Archive for Space Telescopes (MAST)  operated by the  Space Telescope Science Institute (STScI). Funding for the TESS mission is provided by  NASA's Science Mission Directorate. We acknowledge the use of public TESS data from pipelines at the  TESS Science Office  and at the  TESS Science Processing Operations Center. Resources  supporting this work were provided by the  NASA High-End Computing (HEC)   Program  through the  NASA Advanced Supercomputing (NAS) Division at Ames Research Center  for the production of the SPOC data products. Some of the observations in the paper made use of the High-Resolution Imaging instruments 'Alopeke and Zorro obtained under Gemini LLP Proposal Number:  GN/S-2021A-LP-105.
The research leading to these results has received funding from the  ARC  grant for  Concerted Research Actions , financed by the  Wallonia-Brussels Federation. TRAPPIST  is funded by the  Belgian Fund for Scientific Research (Fond National de la Recherche Scientifique, FNRS)  under the grant  PDR T. 0120. 21. TRAPPIST -North is a project funded by the  University of Liege (Belgium) , in collaboration with  Cadi Ayyad University of Marrakech (Morocco ). M. G. is F. R. S. -FNRS Research Director and E. J. is F. R. S. -FNRS  Senior Research Associate. L. D. is an F. R. S. -FNRS   Postdoctoral Researcher. The postdoctoral fellowship of K. B. is funded by  F. R. S. -FNRS  grant  T. 0109. 20  and by the  Francqui Foundation. This publication benefits from the support of the  French Community of Belgium  in the context of the  FRIA Doctoral Grant  awarded to MT.
This research is in part funded by the  European Union 's  Horizon 2020 research and innovation program  (grants agreements n •  803193/BEBOP ), and from the  Science and Technology Facilities Council (STFC ; grant n •  ST/S00193X/1 ). This work is based upon observations carried out at the  Observatorio Astronómico Nacional  on the  Sierra de San Pedro Mártir (OAN-SPM) ,  Baja California, México. SAINT-EX observations and team were supported by the  Swiss National Science Foundation  ( PP00P2-163967  and  PP00P2-190080 ), the  Centre for Space and Habitability (CSH) of the University of Bern , the  National Centre for Competence in Research PlanetS , supported by the  Swiss National Science Foundation (SNSF) , and  UNAM   PAPIIT-IG101321. J. d. W. and M. I. T. gratefully acknowledge financial support from the  Heising-Simons Foundation ,  Dr.
and  Mrs. Colin Masson  and  Dr. Peter A. Gilman  for Artemis, the first telescope of the SPECULOOS network situated in Tenerife, Spain. C. M. acknowledges support from the  US National Science Foundation  grant  SPG-1826583. Research at Lick Observatory is partially supported by a generous gift from  Google. B. V. R. thanks the  Heising-Simons Foundation  for support. F. J. P. acknowledges financial support from the grant  CEX2021-001131-S  funded by  MCIN/AEI/ 10. 13039/501100011033. T. C. H. received funding from the  Europlanet 2024 Research Infrastructure (RI) programme  under grant agreement no. 871149. The Europlanet 2024 RI provides free access to the world's largest collection of planetary simulation and analysis facilities, data services and tools, a ground-based observational network and programme of community support activities.
This work is partly supported by JSPS KAKENHI Grant Number JP18H05439 and JST CREST Grant Number JPMJCR1761. This paper is based A38, page 16 of 20 Barkaoui, K. , et al. : A&A proofs, on observations made with the MuSCAT3 instrument, developed by the  Astrobiology Center  and under financial supports by  JSPS KAKENHI  ( JP18H05439 ) and  JST PRESTO  ( JPMJPR1775 ), at Faulkes Telescope North on Maui, HI, operated by the  Las Cumbres Observatory. U. G. J. gratefully acknowledges support from the  Novo Nordisk Foundation Interdisciplinary Synergy Program  grant no. NNF19OC0057374  and from the  European Union   H2020-MSCA-ITN-2019  under grant no. 860470  (CHAMELEON). This work makes use of observations from the LCOGT network. Part of the LCOGT telescope time was granted by NOIRLab through the  Mid-Scale Innovations Program   (MSIP).
MSIP  is funded by  NSF. This research has made use of the  Exoplanet Follow-up Observation Program  (ExoFOP; DOI:  10. 26134/ExoFOP5 ) website, which is operated by the  California Institute of Technology , under contract with the  National Aeronautics and Space Administration  under the  Exoplanet Exploration Program. K. A. C. acknowledges support from the TESS mission via subaward  s3449  from  MIT. We are grateful to the  ESO/La Silla  staff for their continuous support. We acknowledge funding from the  European Research Council  under the  ERC  Grant Agreement n. 337591-ExTrA. We thank the  Swiss National Science Foundation (SNSF)  and the  Geneva University  for their continuous support to our planet search programs. This work has been carried out within the framework of the  National Centre of Competence in Research  PlanetS supported by the  Swiss National Science Foundation  under grants  51NF40_182901  and  51NF40_205606.
Radiomics has been shown to predict CD8 + T-cell infiltration and response to ICIs or radiotherapy  (9) (10) (11) (12). Additional studies attempted to determine PD-L1 expression, the only approved predictive biomarker in advanced NSCLC  (13, 14). Nevertheless, validation of radiomic models requires large image datasets that include different cancer centers and a variety of computed tomography (CT) scanners. The necessary diversity is a major hurdle to validate published radiomics signatures in independent cohorts  (15). Different image acquisition parameters and different reconstruction kernels with varying slice thicknesses alter the predictive potential of radiomics  (16). Therefore, the development of signatures applicable across academic centers is a challenge that has stymied the adoption of radiomics in routine oncology clinical practice.
Study population This retrospective study included 642 advanced NSCLC patients treated with anti-PD-1 alone or in combination with platinum-doublet chemotherapy between 2015 and 2021 in the chemotherapy-refractory or first-line settings. Signed, informed consent was obtained from each patient, and the study was approved by the Institutional Review Board [Human Ethics Committee (MP-02-2019-8091)] at four academic institutions where patient data were acquired: Centre Hospitalier de l'Universitéde Montreál (CHUM), Jewish General Hospital in Montreal (JGH), Institut Universitaire de Cardiologie et Pneumologie de Quebec -UniversitéLaval (IUCPQ-UL), and Centre Hospitalier de l'Universitéde Sherbrooke (CHUS). All patients with histology-proven stage III or IV NSCLC treated with ICI and with a pre-ICI CT scan were eligible for retrospective review.
From the segmentation, the PyRadiomics library v3. 0. 1, an open-source python package for the extraction of radiomics features from medical imaging, was used to extract 94 candidate radiomics features: 19 first-order features and 75 secondorder features with a Laplacian of Gaussian (LoG) filter applied  (26). The reproducibility of extracting PyRadiomics features from different segmentations has been well studied, and a high reproducibility was reported for first-order, Laplacian, Gaussian-filtered features and texture features, but low reproducibility for shape and wavelet features. Indeed, wavelet features were extracted but omitted from analyses due to the high association with the acquisition parameters.
DeepRadiomics features extraction We proposed a new data-driven alternative to the traditional PyRadiomics method. We followed the emerging interest in deep learning models to provide suitable high-throughput extraction of quantitative imaging features from medical images  (27, 28). A VGG16 backbone was pre-trained to learn image features followed by a SimCLR process, a self-learning framework for contrastive learning of visual representations  (29, 30). The pre-training datasets consisted of the public dataset [Lung Image Database Consortium (LIDC)], after applying the CT scan normalization procedure described in this article  (31) , in order to leverage a larger dataset of lung CT scans also obtained across multiple institutions and acquisition parameters. The pretraining was made on 2D patches of 48 × 48 pixels, centered on the nodules.
Finally, our cross-validation testing allowed for refining the estimated final model with the best average performance remaining within small cross-center variability in performance, thus ensuring optimal generalizability. We used a Sobol sequence for the randomized hyperparameter search to construct low discrepancy sets  (33). The feature selection space was optimized for removing highly correlated features using a Spearman rho method with thresholds ranging from 0. 8 to 1, as well as evaluating the optimal feature reduction method from among (a) F-test, (b) three relief-based algorithms from the open-source library ReBATE (34), or (c) a custom implementation of the Maximum Relevance -Minimum Redundancy strategy  (35). Model selection space was optimized for identifying nonoverfitting methods on our datasets (across folds) from logistic regression and XGboost, and then defining the optimal parametrization of such, considering (a) a metric of calibration with Nagekjerje's R index (average), (b) metrics of discrimination with the area under the curve (AUC), and (c) a metric of goodness of fit with the Brier's score; altogether, the agreement of these metrics is chosen as a proxy for generalizability due to their ability to capture (correlate with) the variance of the AUC (Supplementary Table  1 ).
Radiomics prediction without harmonization Subsequently, we measured the predictive role of radiomics with no harmonization. We used PCoA to facilitate the projection and visualization of high-dimensional radiomic feature data. Before data harmonization, using PyRadiomic features, we observed clustering by CT scan slice thickness, manufacturer, kernel, and academic centers (Figure  2A ). PCoA obtained from DeepRadiomics revealed similar clustering effect across the different medical centers, CT vendors, reconstruction kernels, and slice thicknesses (Figure  3A ). These clusters were expected based on the important difference in CT acquisition parameters from each center (Supplementary Table  3 ). Without CT harmonization, a model combining Clinical + PyRadiomics or DeepRadiomics features to predict PFS-6 featured had an AUC of 0.