dataesr/scientific-paragraphs-categorization huggingface dataset:
Neoadjuvant propensity score matching All patients with data on neoadjuvant therapy use comprised the crude neoadjuvant evaluation cohort. Within this group, a propensity score matching was performed using the clinical factors that are known at the preoperative MDT: sex, age, presence of liver metastases, HIPEC centre, lymph node positive disease, synchronous or metachronous disease, peritoneal cancer index (PCI) as a surrogate marker for preoperative CT-PCI, and the planned HIPEC regimen.
Adjuvant propensity score matching From the crude neoadjuvant evaluation cohort, all patients with missing adjuvant therapy data or with postoperative death within 90 days of CRS/HIPEC were removed to define the crude adjuvant evaluation cohort (Fig. 1 ). Within this adjuvant evaluation cohort, a propensity score matching was performed using the clinical factors that are known at the postop MDT: same factors for neoadjuvant propensity score matching plus completeness of cytoreduction score and Clavien-Dindo grade 3+ morbidity.
Statistical analysis Patient demographics for crude neoadjuvant and adjuvant evaluation groups were calculated together with the two propensity score matched groups. The differences in variables between the two groups in each respective propensity score analyses were calculated using standardized mean differences. There was missing data for timing of peritoneal metastases, primary tumor location, Clavien-Dindo morbidity, adjuvant therapy (only in the neoadjuvant evaluation) and return to OR. These were handled by making the missing data group a separate category. Evaluation of the effect of pre-and postoperative chemotherapy was performed using two analyses. First, a Kaplan-Meier curve with log-rank tests between propensity score matched groups were calculated. Secondly, a multivariable Cox-proportional analysis using all preoperative data and operative data, was conducted for both crude cohorts.
The adjuvant evaluation cohort included also Clavien-Dindo morbidity since this is known to affect the ability to administer postoperative adjuvant therapy. Supplementary figures on the covariate balance after propensity score matching was evaluated using Love plots. All propensity score matching was performed using R statistical software with the MatchIt package. The nearest neighbor method was used with a caliper of 0. 1 for both propensity score matching analyses. Kaplan-Meier curves were rendered, and log-rank tests performed with Statistica v13 software. All data with p-value <0. 05 was considered statistically significant. The proportional hazard assumption was tested with the Grambsch-Therneau test. Using the 10-year follow-up of death events (1399 events) and the 1:2. 6 ratio of no neoadjuvant therapy to neoadjuvant therapy with alpha 0.
Furthermore, we know from the Nordic Peritoneal Oncology Group that most patients from the Nordic centres do not receive neoadjuvant therapy, which is only used selectively in down-staging attempts. This differs significantly from the French centres where neoadjuvant therapy is part of the standard treatment. To account for these differences, matching was performed including HIPEC centres as a matching variable. This also alleviated the centre-related missing data in Fig. 4 , where there is a significant number of patients with missing data on recurrence-free survival (some centre registries have not registered RFS). Lastly, neoadjuvant therapy is connected to down-staging use as well. Patients who are initially large-volume and respond to chemotherapy may still have a worse prognostic outcome.
The complexity of an interpretation can be quantified by the file size of the information carried by its heatmap. In this case, a smaller heatmap file (providing image region scale heatmaps) is likely to be easily interpretable by the layperson  (Narayanan et al. , 2018) , while a complex explanatory output (providing pixel scale heatmaps) is likely more adequate for a domain expert. A comparison of different explanatory output complexity is presented in  Samek et al. (2021) , where LRP  (Monnier & Aubry, 2020)  returns faithful, and complex explanatory outputs on a pixel level, highly suitable for a domain expert. To drive our point, we present a novel approach to historical image analysis that harnesses the explanatory outputs provided by our XAI model (see Section 3), and thus enables correlations to be revealed within a curated dataset of early modern printed illustrations (see Fig.
1 ). Such correlations enable domain experts, in this case historians, to better understand and analyze the content of the dataset at a pixel level. By looking at the insights generated by XAI method, we instrumentally choose a typical question in the frame of material history of science and technology of the early modern period, namely to provide a definition of early modern mathematical instruments. Such a research question, which is developed along the lines of three case studies, is instrumentally used to display the effectiveness of our approach. In this process, we treat the AI model as a helping hand or research companion-in line with similar approaches in medical AI  (Klauschen et al. , 2018; Ratti, 2022)  and cyber analysis  (Holder & Wang, 2021) -that provides suggestions and reveals interesting data correlations that might have been overlooked by the domain expert.
Dataset The robust application of machine learning approaches typically requires a large amount of labeled data to ensure that our explanations are trustworthy, as well as generalizable. To meet this criterion, we rely on the Sphaera dataset, created in the frame of the project "The Sphere: Knowledge System Evolution and the Shared Identity of Europe" (https://sphaera. mpiwg-berlin. mpg. de), which contains data and metadata on over 350 early modern textbooks based on the Tractatus de Sphaera by Johannes de  Sacrobosco (-1256). Electronic copies of these books are available via the project's database, comprising over 70,000 pages, 23,000 of which contain visual elements. These visual elements were collected both manually and with the help of neural networks  (Büttner et al. , 2022). The Sphaera dataset is stored in a large knowledge graph modeled according to the CIDOC-CRM standards  (Bekiari et al.
, 2021) , where information about the editions, as well as fine-grained information about their content is stored  (Kräutli & Valleriani, 2018; El-Hajj et al. , 2022). For the purpose of this paper, and to accomplish our aim to train a neural network capable of correctly classifying pages containing illustrations displaying a mathematical instrument, we initially collected a total of 2,879 pages containing such illustrations, whose labels were carefully studied as part of a PhD dissertation within the Sphere project by  Shlomi (2023). In addition to the pages containing illustrations of mathematical instruments, we collected 3,000 pages that do not contain any illustrations and which serve as a contrast signal to guide the model to learn class-specific instrument features. With such a binary dataset, our neural network, which is based on VGG-16  (Simonyan & Zisserman, 2015)  and further described in Section 3.
1, could successfully distinguish pages containing illustrations of a mathematical instrument from those with no illustrations. We applied the same method in Section 3 on this model, however, the generated explanations we received were not constructive in helping us understand the underlying features that represent a mathematical instrument because the model was simply learning the feature associated with the presence of an illustration vs. non-illustration. To gain further insights into what really defines a mathematical instrument, we created a richer dataset that encouraged our model to learn more discriminative features within these illustrations, which consequently led to more specific insights. To accomplish this, we added two additional classes to our dataset. The first includes images of pages containing scientific illustrations that do not directly denote any material object, such as an instrument, but rather have a descriptive or explanatory function in reference to the subject matter, in this case astronomical phenomena.
These scientific illustrations, similarly to those representing mathematical instruments, were recovered from the Sphaera dataset. The second added class refers to illustrations of material objects, namely those which one can refer to as machines. This data was collected from Branca (1629); Zonca (1607);  Ramelli (1588) ;  Besson (1595)  using CorDeep (https://cordeep. mpiwg-berlin. mpg. de), a web service designed to extract and classify visual elements from historical documents  (Büttner et al. , 2022). In total, the dataset contains 5,879 pages distributed across the four classes, as shown in Table  1 ; each class is represented by a single sample in Fig. 2.
Layer-wise relevance propagation We apply LRP  (Bach et al. , 2015)  to attribute the predictions of our trained neural network to the input features (i. e. , pixels). More specifically, by feeding a given input image to the network and denoting by y the resulting value of the output neuron for a given class, say instrument, LRP generates a collection of scores R 1 , R 2 ,. , R # pixels identifying the contribution of each pixel to the output value y. This collection of scores can be represented as a heatmap in which the blue color indicates pixels that contribute negatively to the given class (i. e. are in contradiction with it), and red color indicates pixels that contribute positively (i. e. support it). Because there are four classes in our dataset (other, mathematical instruments, machines, scientific illustrations), we generate four heatmaps for each image indicating pixels that support/contradict the respective class (see Fig.
Three case studies and the quest for early modern mathematical instruments By applying the method from Section 3 on the dataset described in Section 2, we obtain a treasure trove of information about the image pixels with the highest contributions to the learning task. In the following section, the model explanations are discussed in reference to the predefined classes in Section 2 (excluding the category "other"), and therefore subdividing the argument into three case studies. Due to the complexity of the LRP explanations, interpretations are usually formulated by domain experts, who in our case are historians of early modern science.
The classic definition of an early modern machine is that of an object that enables the accomplishment of a task by means of the efficient performance of a mechanical device and thus lowering the need of human resources and/or reducing the time required for its performance. This definition is too general, and does not allow us to hone in on what constitutes a machine in our training set. Our trained model was able to perfectly classify the machines in our dataset, and by looking at the LRP explanatory outputs, we were able to deduce that the most influential elements driving the correct classification of a machine-class within our dataset are the pixels representing aspects of the mechanical apparatus on the one hand, and the presence of a structured environment on the other. If a machine is analyzed in a representation that depicts it in its natural or semi-natural context, then the mechanical apparatus is activated.
There are also misclassifications with respect to the early modern machine class such as the case presented in Fig. 10a. This is a lathe equipped with a semicircular blade which is rotated by means of a handle. Material (wood or stone) roughly spherical in shape is placed inside this machine, which then refines the object's shape (by cutting the excess) to produce an almost perfect spherical shape. The example of the lathe machine is inserted in the historical sources analyzed here because it was used to furnish an operational, almost material definition of what a sphere is: the fundamental geometric concept to understand the cosmos and the working of the machina mundi. For this reason, this machine is printed numerous times in the Sphaera corpus. The multiple instances of this machine appearing in our dataset were consistently classified as mathematical instruments.
The goal of the presented historical research was to provide a definition of early modern mathematical instruments. These illustrations within our corpus often represented real objects; however, only a small fraction of these objects survives today. Museum collections contain some of these objects, but their number is limited, no complete dataset has been created, and, most importantly, even if such a dataset existed, it would not cover the myriad of instruments that populated the early modern period. Faced with this situation, historians of science and technology rely, as in the case of this work, on representations of such objects. These are abundant in the numerous preserved historical textual sources. The massive digitization efforts of the last twenty years, moreover, have made them largely and easily accessible.
First of all, the represented object, such as a machine or an instrument did not have to exist. It could be the illustrated design of a new instrument, namely just a mental exercise. From this perspective, the analysis looks at what elements made a printed illustration a mathematical instrument illustration. Second, even in the cases where the represented object existed, there may no longer be a physical counterpart. In other words, in many cases, there is no extant artifact that enables us to compare the real physical instrument with its representation in the Sphaera dataset, meaning that abstractions are often not verifiable. This condition, combined with the high variability of instrument types and the consequent low number of images per specimen, makes working with this kind of datasets quite challenging.
Visual conventions are structures or symbols that are widely used, and can thus be better "exposed" with the use of large corpora rather than a more traditional and manual examination of single sources. Although they do not necessarily represent objects that in fact existed, such conventions may reflect common knowledge used in printed books of the period and is thus meaningful in the study of the knowledge tradition. Beyond the study of visual language for itself, the analysis of the XAI explanatory output for the study of mathematical instruments highlighted that the analysis of historical sources requires the consideration of all intrepretational layers or, in other terms, that both the form and substance of the images need to be considered at the same time for this kind of research. Considering the visual language can be a tool for better defining and studying mathematical instruments through the database.
Some of these attempts emphasize a semiotic approach that highlights the variety of types of similarities between objects (historical source or idea) and their representations (either representations of single objects or of relations between historical objects or ideas)  (Kralemann & Lattmann, 2023; Ciula & Eide, 2016; Flanders & Jannidis, 2023). In the context of this research, we have seen that it is crucial to consider and strictly define whether the object falls under the definition of mathematical instruments or of scientific illustrations and the visual conventions they include. Furthermore, we had to also consider the nature of the relationship and similarity between the two. Thus, this research demonstrates the importance of a semiotic discussion in the process of building a training set, database, and research questions based on them.
Conclusion We applied a XAI approach, more precisely an LRP approach to our classification model in order to explain how and why it classifies the illustrations of our corpus. We intentionally trained our model based on a specific, curated, dataset with carefully chosen classes in order to help us gain insights concerning our initial research question, namely what is an early modern mathematical instrument and, as our research shows, what differentiates it from an early modern machine and an early modern abstract scientific illustration. While we were able to achieve interpretable and useful results, we were also faced with unexpected outputs that obliged us to reevaluate our class definitions. As we are using illustrations of instruments and machines, XAI has shown us that we need to consider not only the proper content of those illustrations but also the visual conventions used by the historical actors to produce them.
Data remain capta, taken not given, to use Johanna Drucker's definition, whether XAI is used to make its decisions transparent or not  (Drucker, 2011). As always in data-driven research, the data must therefore be chosen carefully, and its selection and effect on the application of XAI must be taken into consideration when studying its decisions. Nevertheless, this contribution shows that engaging with the interrogative approach engendered by XAI and taking its classification decisions as a productive "derivation of the eye," can produce a different way of seeing and thus provide a new perspective on the classification of historical sources. In fact, the presented approach is not limited to early modern illustrations but can provide relevant insights (depending on the quality of the dataset used) in numerous humanities fields.
Model and optimization We utilize a standard pretrained VGG-16 model that was originally trained to separate between 1,000 classes of natural images of objects  (Simonyan & Zisserman, 2015). We replace the last linear layer with one that has as many output neurons as there are classes, which corresponds to four in our case. To predict these four distinct classes with high accuracy, we finetune the parameters of the final classification layer using the training dataset. During optimization, we minimize the cross-entropy loss between true and predicted labels using the Adam optimizer  (Kingma & Ba, 2015)  with a learning rate of 1e -3 , stepwise learning rate decay every sevens epochs by a factor of 0. 1 for a maximal number of 25 training epochs. We use a batch size of 1 to allow for different page sizes and orientations, and measure a test set accuracy of 0.
Learning and Data (grants 01IS18025A and 01IS18037A), the Rotenstreich fund, and by the Max Planck Institute for the History of Science. Furthermore Klaus-Robert Müller was partly supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grants funded by the Korea Government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University and No. 2022-0-00984, Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation).
Olive leaves Olive leaves of Picholine and Olivière varieties (used for the production of olive oil) were collected in March 2022, in the Hérault department, of the Occitanie region of France (latitude 43. 5912356, longitude 3. 2583626). The leaves were immediately removed and stored in cardboard boxes protected from light and humidity until analysis in June 2022. Leaves of four other, mainly ornamental varieties, were collected locally and analysed in order to provide additional data for the validation study; These varieties could not be identified, but insofar as could be established, they are Italian or Greek hybrids; in the interest of simplicity, these varieties were named UNK 1, UNK 2, etc.
Search Strategy Systematic searches were conducted using six scientific databases (PubMed, Embase, Epistemonikos, PEDro, COCHRANE Library, and Scopus) and cross-referencing. We reviewed articles written in English and French published up to 24 August 2023. The search terms were adjusted for each database (see Appendix A) and included: ("functional electrical stimulation") AND ("healthy" OR "able-bodied" OR "normal" OR "non-disabled") AND ("gait" OR "walking" OR "locomotion"). Two independent reviewers (TA and TC) independently screened articles according to the eligibility criteria.
Methodological Quality Assessment The methodological quality of the included studies was assessed independently by two researchers (TA and TC) using the Physiotherapy Evidence Database (PEDro) scale. The PEDro scale is a commonly used checklist consisting of 11 items rating the external and internal validity of studies. The total score of the PEDro scale corresponds to the number of "yes" responses for items 2 to 11, i. e. , a total score expressed on 10 points. The first item is not included in the sum of the total score because it is the only item related to external validity  [19]. Studies scoring 6 were considered "good" quality, those scoring 4 or 5 "fair" quality, and those scoring <4 were considered "poor" quality  [20]. Any discrepancies between the two reviewers were resolved through discussion and mutual agreement or by a third researcher (MFS) providing a rating.
Search Results A total of 2583 records were initially identified from six databases. After removing 498 duplicates, 2085 studies underwent screening. Based on titles and abstracts, a blinded selection among these 2085 remaining studies was conducted. After analyzing the full texts, 15 studies met all criteria and were included in this systematic review. The flow diagram of the screening procedure is presented in Figure  1. Extracted data from the included studies are reported in Table  1.
Participants A total of 215 healthy participants were included in these 15 studies. Fourteen studies examined the effects of FES on gait characteristics in healthy young individuals (186 participants; mean age: 25 years), whereas only one study investigated the effects of FES in the elderly (29 participants; mean age: 75 years)  [16]. Two of these studies focused on the effects of FES during walking in healthy young subjects, comparing them to pathological populations such as post-stroke patients and individuals with chronic ankle instability  [27, 31]. In this systematic review, only data on healthy participants were considered and are summarized in Table  1.
1  given the (astronomical) broadening of the lines. Classified as ON9. 7 Ia+ (and with log(L/L ) ≈ 6) the UVES data of HDE 269896 reveal the high Balmer series and a plethora of He I lines, together with weak emission lines from Si III (λ3487, 3590, 3807), Si IV (λ3762 and 3773) and Al III (λ3602) produced in its strong stellar wind (see  [9] ). The presence of lines from two ionisation stages of silicon offers a potential temperature diagnostic in the CUBES region, albeit very dependent on the adopted stellar parameters (including the wind) when the lines are in emission. To illustrate the ability of sophisticated model atmospheres and spectral synthesis to reproduce this region in O-type spectra (including the higher-order members of the Balmer series), in red in Fig. 1  we overplot the adopted cmfgen model (see  [10] ) for this star from  [9].
LMC metallicity: temperature trends The metallicity of the Large Magellanic Cloud (LMC) is approximately half solar and we have a good understanding of stellar properties from analysis of data at other wavelengths (e. g. [12] ). We therefore first considered the 0. 5 Z models from the OSTAR2002 grid as example sub-solar models with which to investigate the CUBES domain. We initially considered models with logg = 4. 0, that are typical of the gravities estimated for dwarfs  [14]. The tlusty grid has models from T eff = 27,500 to 55,000 K (in steps of 2,500 K). Using the effective temperature -spectral type relations derived from analysis of O-and B-type dwarfs in the LMC  [14, 15] , we can map the tlusty models to approximate spectral types. The near-UV and visible ranges are shown for eight model spectra in Figs.