dataesr/scientific-paragraphs-categorization huggingface dataset:
Thermodynamic modeling The isochemical phase diagrams were computed using the Gibbs free energy minimization THERIAK-DOMINO package (version 04. 02. 2017)  [de Capitani & Brown, 1987; de Capitani & Petrakakis, 2010]  using the internally consistent thermodynamic dataset ds55  (update Nov. 2003 of Holland & Powell, 1998). Samples S510, S508, and S520 were modelled in the Na 2 O-CaO-K 2 O-FeO-MgO-Al 2 O 3 -SiO 2 -TiO 2 chemical system using the local bulk compositions extracted from the X-ray compositional maps. The following mixing models for solid solution were used: garnet, biotite, and melt  [White et al. , 2007] ; white mica  [Coggon & Holland, 2002] ; ilmenite-hematite  [White et al. , 2000] ; amphiboles  [Diener et al. , 2007] ; clinopyroxene  [Diener & Powell, 2012] ; feldspars  [Holland & Powell, 2003] ; epidote, talc, and chlorite  [Holland & Powell, 1998 ].
DATA ACQUISITION All women over 18 years of age who underwent pelvic MRI, including sagittal and axial T2-weighted images, in the women's imaging department of Valenciennes Hospital (France) between September 2021 and March 2022 were retrospectively collected from the Institutional Picture Archiving and Communication System (EMR Manager, VEPRO AG, Version 8. 2, Germany). Pregnant women were excluded, as were MR images with severe motion artefacts, a highly deviated uterus, and subserous myomatous pathology (FIGO VI and VII) These examinations were performed using two MR units, either at 1. 5 T (SIGNA artist) or 3 T (SIGNA premier) (General Electric Healthcare, Cleveland, USA). The acquisition parameters are listed in Table  1. Slice thickness (mm) 3. 5-4. 0 3. 5-4. 0 3. 0-3. 5 Interslice gap (mm) 3.
DATA LABELLING One radiologist (DM) used an arti cial intelligence software speci cally designed for medical imaging (Cleverdoc V1. 9. 0 platform) for labelling. When the uterus was not seen, a label of "not observable" was attributed to the examen. When visible, it was targeted by a square area (label box). Three consecutive cuts were annotated with points corresponding to the measurements of the uterus (Fig. 1 ). Thickness and length were noted on sagittal T2-weighted images. The length was measured using two different methods: one major axis de ned by two points, and the other de ned by three points passing through the fundus, the cervix-isthmus junction, and the exocervix (preferred when the uterus is considerably exed) (Fig. 2 ). The width was labelled on the axial T2-weighted images.
Model testing Four experts with experience in genital imaging (EP, GR, BH, and LD with 12, 2, and 1 years of experience, respectively) manually measured the size of the uterus in three dimensions on every MR image of the test set. The radiologists were blinded to the measurements made by others and to the machine. The same viewer (ViewerCleverdoc1. 9. 0) was used. = exp(- ) d 2 2s 2 k 2 We calculated the absolute average difference (in millimetres) between the measurements of one dataset and those of another. The coe cient of determination R² was used to re ect how well the AI measurements matched those of the radiologists. If R² equals 1, the algorithm obtains strictly identical measurements to those of the radiologists. The Cleverdoc V1. 9. 0 platform (Lille, France) was used for the statistical analyses.
Validation Performance ( initial dataset) During the validation phase, the algorithm was able to locate the uterus and the measurement keypoints with excellent accuracy. With the measurement by DM as ground truth, the mean OKS was 0. 92, ranging from 0. 90 and 0. 94 (Table  3 ). The OKS was calculated using the cropped images. We observed an improvement in the accuracy of our model when we switched to a new unknown cohort for testing. With the average of radiologist's measurements as ground truth, the mean OKS of our DL tool was 0. 96, ranging from 0. 95 and 0. 98, as reported in Table  4. The OKS was calculated using full-size images. Regarding the execution speed, it took less than 5 min for the model to extract all measurements, that is, one three-dimensional measurement in approximately 1.
To the best of our knowledge, only one study on uterine segmentation has been conducted to date. Kurata et al. evaluated a U-net architecture to contour the uterus on MR images  [10]. They reached an average DSC score (dice similarity coe cient, which can be compared to OKS) of 0. 82. This study included 122 patients with uterine disorders. Our model was optimised by using a substantially larger training database of 800 patients. In parallel, for men, a wide range of studies have been carried out on the automatic segmentation of the prostate, with similar results. For example, Alexander Ushinsky et al. trained a customized hybrid U-Net CNN architecture on manually segmented MR images and had a DSC score of 0. 898  [11]. However, it is more complex for an AI tool to locate and segment the uterus than of the prostate, because it can have different positions, bends, or shapes.
Moreover, the uterus is surrounded by many elements (colon, bladder, ovaries). Another highlight of our study is that our training dataset was strengthened by the clinical heterogeneity of its cases, both in terms of pathological conditions and patient preparation. It included cervical cancer, endometriosis, and rectal vaginal opaci cation. This suggests that the performance of our CNN would be robust in prospective clinical settings. Most studies on automated segmentation have used volumetric models or U-Net architectures. In contrast, our network's performance was achieved with the VGG-11/16 architectures. This model is more suitable for distance measurements, because it is speci cally designed to locate an organ and place measurement points on it. To do so, our pipeline operates using two different models.
For these simulations precision and speed are essentially two sides of the same medal. Adding modern machine learning to the numerics toolbox will help provide the simulations needed for the LHC Run 3 and HL-LHC  [6] , as well as future energy frontier machines. From a fundamental physics perspective there exist three distinctly different kinds of measurements at the LHC. First, basic and purely experimental measurements should be as independent of theory considerations and first-principle simulations as possible, to avoid expiration dates. Their problem is that they provide no information about fundamental physics. These basic measurements benefit from modern machine learning for instance in understanding the data and calibrating the detectors. A second class of measurements is supplemented with a fundamental theory interpretation framework.
Such measurements of all possible features in the vast phase space of LHC collisions require precision simulations, specifically theory-based event generators. We will show how all of these aspects benefit significantly from the application of modern machine learning methods. The challenges for event generators are, first of all, defined by the increase of the LHC luminosity and the expected advances in experimental precision and reach. Going from the Run 2 dataset of 139 fb -1 to the projected HL-LHC dataset of 4 ab -1 suggests that experimental uncertainties at and below the percent level will become standard and need to be matched by theory predictions, to allow for any kind of precision measurement. The same increase in rate will allow us to probe more and more exotic kinematic regions, with the hope of finding hints for new particles and interactions.
To exploit the vast dataset of the coming LHC runs and optimize the design of a possible future collider, we must sustain the extremely fruitful exchange between particle physics and AI/ML research. One way to cultivate and capitalize on these ideas is to create a world-wide research network for AI/ML-related fundamental physics, through targeted funding calls and long-term research structures at laboratories and universities. Such a network would open attractive career paths for young researchers in the high-energy physics community, establish crucial ties between particle physics and AI/ML research, and foster collaborations between academia and industry.
A second approach to ML-assisted phase space sampling is based on directly learning the phase space distribution of events from input training samples, either weighted or unweighted. Solutions employ autoregressive flows  [16] , generative adversarial networks (GANs)  [17] [18] [19] [20] , or variational autoencoders (VAEs)  [19]. This motivates R&D to improve training through differentiable programming; by merging matrix element codes with automatic differentiation  [21] , i. e. the automatic generation of derivatives of programs that is the backbone of neural networks software frameworks. The gradients of matrix elements can be evaluated and used as additional information for training generative models. Initial studies using differentiable matrix elements from MADJAX have explored extending normalizing flow training with schemes uniquely enabled by the ability to automatically compute matrix element gradients  [22] , and show promise in terms of improving modeling and reducing the needed scale of simulated datasets for training.
Parton distribution functions Parton distribution functions (PDFs), encoding the structure of colliding protons, are vital for the calculation of hard scattering cross sections at the LHC and appear in several stages of the event simulation chain, in particular they guide the initial state parton showers and affect the underlying event activity. The determination of PDFs is a classic pattern recognition problem: it is known that an underlying law exists (the true analytic form of the PDFs, as determined by QCD in the non-perturbative regime) but its explicit form is not known, and it must be inferred from discrete data (the cross-sections of PDF-dependent hard processes), that moreover are correlated to it indirectly and in a convoluted way. In comparison to more standard pattern recognition problems, it has two peculiarities.
Fragmentation functions Fragmentation functions (FFs) are the time-like equivalent of PDFs and encode the probabilities associated to the transition between partons produced in the hard-scattering and specific types of hadrons. Being based on the perturbative QCD factorization framework, FFs represent an alternative strategy to model partonic hadronization as compared to the phenomenological models available in most MC event generators. FFs can be determined from a global analysis of hard-scattering data from electron-positron collisions, semi-inclusive deep inelastic scattering, and proton-proton collisions (RHIC and LHC) with identified final-state hadrons. A phenomenological analysis of FFs requires introducing a parametrization for their initialscale (Q 0 ) dependence with the momentum fraction z, z D (h) i (z, Q 0 ) , where i is a partonic index and (h) a hadronic label.
End-to-end ML-generators In addition to applying a wide range of machine learning tools to improve the modules of classic event generators, we can train generative neural networks to directly generate events, at parton level and with or without detector effects  [6]. End-to-end or, better, soup-to-nuts MLgenerators have to be developed together with the established generators and serve as studies for phase space generators, enable inverted simulations, provide datasets for phenomenological analyses, and allow us to efficiently ship event samples. Their advantages include training on data combined with simulations, manipulation of event samples  [63] , or post-processing of MC data for example to unweight events  [16, 23, 25]. Finally, they define useful benchmarks for conceptual work on uncertainty estimates for generative neural networks.
In Ref. [99] , particle reconstruction was recast as a computer vision problem using state-of-the-art ML architectures including U-net, graph neural network (GNN) and DeepSets. A simplified dataset was used comprising overlapping pairs of charged and neutral pions in a 6-layer calorimeter block. In comparison to a traditional PF algorithm, the ML models regress the component of neutral energy better by a factor of two to four in terms of resolution. The study also finds significant improvements via a super-resolution approach (see also Ref. [102] ), where the network is trained to predict a corresponding calorimeter signature with higher granularity. This proof of concept has been extended to particle reconstruction in more realistic environments resembling multiple pileup interactions in a full-coverage simulated detector  [100, 101].
Detector unfolding While the physical processes behind an LHC collision are described by fundamental physics and are therefore universal, the observed data depend in an intimate way on the technical details of the detector. Detector effects like phase space coverage, detection thresholds, particle reconstruction, efficiencies, or calibration induce not only resolution smearing in the measurements, but can lead to systematic deviations between the properties of particles reaching the detector and the objects reconstructed from actually measured data. For individual experiments, these detector effects differ greatly and can only be estimated by the collaboration. It is therefore essential for future interpretations of a measurement to unfold detector effects so that we can compare measurements by different experiments to each other and to theory predictions.
Synergies, transparency and reproducibility A key paradigm in the development of simulation tools for high-energy collider experiments is publicly accessible open source software. The versioning of code releases and the reproducibility of predictions is vital for a reliable analysis and interpretation of collider data. As we have seen in the previous sections, ML-methods are entering all aspects of the simulations chain at high pace. They range from initial proof-of-concept applications to well established use cases with largely consolidated techniques, for example in the determination of parton densities. Machine learning models efficiently encode arbitrary decision functions of a given set of inputs, and thus offer a chance to easily exchange complex relations. This might correspond to the value of a scattering matrix element given a set of momenta, or probability models from simulation-based inference, like MEM or MadMiner.
On the side of results by large collaborations, this adds additional weight to the ongoing move towards publishing open data along with measurements. Containerization, as enabled by software tools like Docker can be useful in bundling the correct versions of different software packages used for data processing and machine learning in a coherent fashion. An opportunity exists in the realm of phenomenological studies based on the DELPHES detector simulation  [121]. Here a common specification on how quantities are translated into the inputs to machine learning algorithms might -together with publishing the ML modelsboost sharing and meaningful exchange. Another interesting angle are generative models. As these do not need data to evaluate, sharing the architecture and weights is already sufficient.
Generative networks themselves can even be used as an efficient alternative way of sharing simulated data. Publication of ML models for their reuse is not yet standard in the particle physics community. Examples where trained networks have been published in ONNX format for future reuse are the DNNLikelihood  [122] , a package for cross-disciplinary training of discriminator networks  [123] , and the ATLAS search for R-parity-violating supersymmetry  [124, 125] , the latter also being available in the ATLAS SimpleAnalysis framework. However, detailed documentation for instance of the input variables is missing. Further development is strongly encouraged for, e. g. , the purpose of analysis preservation  [126, 127] , and in general for the implementation of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles for scientific data management  [128]  of ML models.
Outlook As a field combining vast datasets with excellent, first-principle simulations, particle physics is benefiting tremendously from developments in data science and machine learning. While new AI-inspired methods will not magically solve all challenges in LHC simulations and analysis, they are providing a crucial and transformative contribution to our numerical toolbox. Moreover, given the quality of the LHC datasets, simulations, and simulation-based analysis methods, we expect particle physics to eventually contribute to broader machine learning research. Event generation, or the simulation of signals for the LHC detectors from QFT Lagrangians, is the main link between experimental and theoretical particle physics. It has stringent requirements when it comes to first principles vs modeling, control, precision, speed, and flexibility.
This includes conceptual developments in the field of generative networks, driven by LHC-specific requirements of controlling precision-generative networks as numerical tools and providing a full range of uncertainties. They allow us to define, produce, and encode datasets for phenomenological studies and serve as a compression for data entering experimental analyses. The main conceptual advantage of ML-event generation is that simulations with generative networks are symmetric: given a fundamental physics model we can predict the probability distributions of LHC events over phase space, or we can predict the probability distributions of model parameters given observed LHC events. Different ML-approaches to simulation-based inference include classification-based methods, conditional generative networks as a direct inversion, or indirect ways of learning likelihood ratios.
In combination, they will allow us to systematically use unfolding or inverted simulations at the HL-LHC, from particle identification and detector unfolding all the way to an event-wise matrix element method analysis. Finally, there are many simulation-related questions in fundamental physics, where AImethods allow us to make significant progress. Examples going beyond immediate applications to event generation include symbolic regression  [130] , sample and data compression  [57, 131] , detection of symmetries  [132] [133] [134] [135] , and many other fascinating new ideas and concepts.
Acknowledgments Anja Butter ,  Gudrun Heinrich , and  Tilman Plehn  are supported by the  Deutsche Forschungsgemeinschaft  under grant  396021762 -TRR 257   Particle Physics Phenomenology after the Higgs Discovery. Lukas Heinrich  is supported by the  Excellence Cluster ORIGINS , which is funded by the  Deutsche Forschungsgemeinschaft  under Germany's Excellence Strategy -EXC-2094-390783311. Alexander Held is supported by the  U. S. National Science Foundation (NSF)  Cooperative Agreement OAC-1836650. Stefan Höche  and  Joshua Isaacson  are supported by the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. Jessica N.
Howard  is supported by the  U. S. National Science Foundation  under the grant  DGE-1839285. Michael Kagan  is supported by the  US Department of Energy (DOE)  under grant  DE-AC02-76SF00515. Gregor Kasieczka  and  Felix Kling  are supported by the  Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy -EXC 2121 Quantum Universe -390833306. Sabine Kraml  acknowledges support by the  IN2P3  master project  Théorie -BSMGA and the joint ANR-FWF  project  PRCI SLDNP grant  no. ANR-21-CE31-0023. Claudius Krause  and  David Shih  are supported by  DOE  grant  DOE-SC0010008. Rahool Kumar Barman  and Dorival Gonçalves thank the  U. S. Department of Energy  for the financial support under grant number  DE-SC 0016013. Benjamin Nachman  is supported by the  U. S. Department of Energy, Office of Science  under contract  DE-AC02-05CH11231.
Design: To perform this design, we have used a classical thin-film approach based on matrix formalism  [8]  to  design and calculate a simple multilayer structure that would allow for the minimizing of the reflection coefficient in the 8-12µm range for angles of incidence ranging from 0° to 30°. ZnS and YF 3 were chosen as high and low refractive index materials respectively because they are classical infrared materials that both exhibit low absorption in the LWIR range. Two different structures were designed in the OptiLayer software using a needle approach  [9]. First, a 6-layer anti-reflection was developed. Then, in order to limit the total stack thickness, the number of interfaces, and the bending caused by the stress and temperature dependence, the stack was eventually limited to a simpler 2-layer design with formula 1.
04 H, 1. 04 L where H represents a ZnS quarter wave layer and L a YF 3 quarter wave layer, both at 10 µm. The refractive index at 10 µm wavelength of the ZnS and YF 3 layers are equal to 2. 257 and 1. 363 respectively  [10]. The thicknesses of the layers are equal to 1183 nm and 1935 nm respectively (see table  1 ). This solution represents a compromise between minimizing the total thickness and minimizing the residual reflection of the final ARC. To verify the efficiency of the coatings, simulations were performed using the Optilayer software and the results for normal incidence and 30° incidence are presented in Figure  2  below. We can see that the 2-layer coating theoretically reduces reflection to under 2% for a single air-Si interface, which is a great improvement compared to the original ∼30% of the bare Si wafer.
Acknowledgments: This work was carried out with funding by the  ECSEL Joint Undertaking (JU)  project  APPLAUSE  under grant agreement No  826588. The JU receives support from the  European Union 's  Horizon 2020 research and innovation program. The  Research Council of Norway  is also acknowledged for its support to APPLAUSE and the  Norwegian Micro-and NanoFabrication Facility  ( NorFab , project number:  245963/F50 ).
To assess the potential effects of hTR on tumor dissemi nation, visceral organs with gross tumors were quantified during necropsy. While wt virus-induced tumors disseminated to various organs, vhTR and v∆vTR tumors were restricted to a single organ per animal (Fig. 3D ). To ensure that hTR was efficiently expressed in vhTR-induced tumors, we performed RT-qPCR on the tumor tissues. RT-qPCR revealed that hTR was efficiently expressed in vhTR-induced tumors at levels comparable to vTR in wt virus tumors (Fig. 4A  and B ). Our data demonstrate that hTR does not drive tumor formation in our natural virus-host model of herpesvirus-induced tumorigenesis.
Since TRs share the main functional domains, we investigated whether hTR also possesses pro-oncogenic properties in our natural virus-host model of virus-induced tumorigenesis. We generated a recombinant virus expressing hTR instead of vTR. Neither vTR deletion nor hTR insertion altered viral replication in vitro or in vivo. This supports our previous finding that vTR is dispensable for MDV replication  (14, 21). To confirm the efficient expression of hTR in vhTR-infected cells, we used RT-qPCR. Our data revealed that hTR was efficiently expressed in the vhTR-infected cells. This expression was comparable to that of vTR in the wt virus, as both vTR and hTR were driven by the same promoter in the viral genome. To investigate whether hTR could complement the loss of vTR in MDV-induced oncogenesis, we infected chickens with the recombinant viruses.
Statistical analyses Statistical analyses were performed using the SPSS software (SPSS, Inc. ) and GraphPad Prism v. 7. The data were first analyzed for normal distribution. Plaque size assays were performed using one-way analysis of variance. qPCR data were evaluated using the Kruskal-Wallis test. The results were considered statistically significant at P < 0. 05. Fisher's exact test and Kaplan-Meier survival analysis using log-rank test (Mantel-Cox test) were used for animal experimental data as indicated, with the Bonferroni correction for multiple comparisons, and were considered significant if P < 0. 0125.