dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, the fixation and/or material properties of the transparent material, the stiffness of the rods, the modeling of the water filling of the rods, and dimensional tolerances of the components of the experimental set-up might play a role  [28]. 4 Pool thermal hydraulics SESAME work package number three, deals with HLM flows in a pool configuration at different scales (Fig. 5 ). Thermal stratification and mixing phenomena were  Experimental data were used to validate numerical approaches developed in parallel for these facilities using CFD software. These comparisons, reported in  [30, 31]  show reasonable performance of the CFD models. In  [30]  validation of CFD was performed for the TALL facility including an elaborate sensitivity analysis. This analysis indicates that the boundary conditions (e.
System thermal hydraulics In the frame of safety assessment and design of nuclear reactors, the use of system thermal-hydraulics codes is widely adopted to simulate the transient behaviour of the whole systems, i. e. primary and secondary system, including the balance of plant. Such STH-codes have been developed mainly for PWRs and BWRs (e. g. RELAP5, CATHARE, etc. ), and validated using integral test facility design specific or experimental data coming from the operation of nuclear reactors and prototypes  [33]. For the application to liquid metal fast reactor, these STH-codes need to be updated with state-of-the-art algorithms, models and correlations, and their validation extended with suitable experimental database aiming at confirming their applicability for safety analysis. Moreover, in the case of multi-scale approaches, in which STH-codes are coupled with CFD codes to catch relevant 3D phenomena in the system simulation, the validation process has to be further extended considering the code coupling.
The multi-scale approach is going to be developed both for light water  [34]  and liquid metal cooled reactors  [35]. For liquid metal systems very few data set are available for the validation process, as for example the data coming from the experiments performed on TALL-3D loop. Apart from this small scale basic experiment, validation of such multi-scale approaches has also been performed by comparing to reactor scale data from the EBR-II  [36]  and Phénix natural circulation tests  [37]. As these data relate to real operating reactor, the possibilities for instrumentation were limited. One of the main goal of SESAME project was to extend the validation base of STH-codes or multi-scale approaches, providing suitable experiments for the aim (see Fig. 7 ). The first level of validation data was provided by experiments performed by TALL-3D (KTH, Sweden) and NACIE-UP (ENEA, Italy) loop facilities.
Conclusions The activities and progress in support of liquid metal cooled reactor design and safety analyses performed within the European collaborative H2020 SESAME project are described in this paper. The major outcomes are: -Turbulent heat transport in liquid metal: • Enlargement of the reference database with new experimental and high fidelity numerical data with a focus on flow separation, jets, and rod bundle flow phenomena. • Further development and assessment of promising models like a second order heat flux model, implicit and explicit algebraic heat flux models and the application of the Kays correlation. -Core thermal hydraulics: • Creation of new experimental and high fidelity numerical data for validation of RANS models with respect to the hydraulics of the flow in wire wrapped fuel assemblies.
Altogether, these data showed that a natural variation was present in the major immune cell populations in the group of 16 subjects. the proliferation of C. albicans clinical isolates into leucocyte co-cultures is characterized by specific persistence indexes. Leucocyte cells were challenged by three C. albicans strains. For each clinical isolate, the fungal growth was followed at different time points. The killing activity of human immune cells was measured by culturing living yeasts from co-cultures through a colony-counting technique. Results were expressed as the means of colony-forming unit (CFU/ml). Figure  2A  depicts the CFU/ml means over the 6 days post-infection of PBMCs for each subject over the 3 months of sampling and each clinical isolate. The results showed a fungal burden reduction of the three C.
The post-challenge variability was quantified as the sum of squares of the differences between the column means and the grand mean. The F ratio was the ratio of two sum of square values. Large F ratio signified higher post-challenge variability than inter-subject variability. The P value was determined from the F ratio and the two values of degrees of freedom. The relative contributions of inter-subject variability and C. albicans challenge to the total observed variation in the immune parameters was measured on Prism 6. 0a by calculating the range of subject-to-subject differences (total sum-of-squares), n = 16, p ≤ 0. 05. Immune cells frequencies data were at this time point. Day 6 responses primarily reflected changes in adaptive T cells, including an increase of the CD4+/CD8+ ratio in the CI outcome compared to the PI outcome (CI ratio of 4.
Discussion During the host-C. albicans interaction, several parameters predispose to fungal disease, in particular C. albicans pathogenicity and host risk factors, such as immunosuppression (co-infections, immunosuppressive treatments and medical interventions)  4, [33] [34] [35]. However, these factors alone do not fully explain why, under comparable risk factor conditions, only some patients develop infections  [7] [8] [9]. Considering that Candida infections can be raised not analyzed according to UI controls, CI and PI outcomes for Caal93, Caal121 and Caal123 clinical isolates. (B) Global phenotypic modifications of immune cells induced by C. albicans over the post-challenge time. Data from all of subjects and clinical isolates were concatenated and analyzed on tSNE FlowJo module according to PI and CI outcomes and UI controls.
Immune cells frequencies data were analyzed according to UI controls, CI and PI outcomes. *P < 0. 05; **P < 0. 001; ***P < 0. 0001; ****P < 0. 00001; ns = not significant. only because of fungus-mediated damage but also because of host-mediated damage or both, research efforts into the pathophysiology of human C. albicans infections should include the understanding of fungal pathogenicity in the context of the specific patient's immune response. The major goal of this study was to probe the relevance of the C. albicans persistence model to monitor the dynamics of fungal and human immune cell interactions. To begin to model these complex interactions, we took into account the diversity of human immune responses after C. albicans challenge (subject-to-subject variability) and the temporal changes around the baseline parameters for each subject (within subject variability).
The tSNE algorithm is a powerful data visualization and hypothesis generation technique. In order to visualize large datasets from multiple measurements, we integrated the tSNE algorithm to the study of the diversity of C. albicans persistence abilities during the interaction with immune cells. To our knowledge, this is the first study investigating the intra-and inter-individual diversity of human innate and adaptive immune responses against the diversity of C. albicans isolates. The input factors were normalized between uninfected and Candida-infected conditions in order to compare relative immune cell abundance and cytokine secretion. In the presence of highly conserved tSNE signatures for each subject over the sampling time period, our results allowed us to formulate hypothesis about the phenotypic modification induced after C.
Given that the dimensionality reduced data suggested the presence of different cell subsets, we next evaluated functional changes in the secretion of cytokines by immune cells across subjects. With regard to the current knowledge about the essential role of cytokines during fungal infections, we also observed that C. albicans challenge induced a myriad of pro-and anti-inflammatory cytokines  15, [48] [49] [50]. This C. albicans persistence approach allowed the analysis of cytokine secretion according to innate myeloid cells and adaptive CD4+ and CD8+ T cells. The frequencies of cytokine expressing cells significantly varied across subjects but were conserved within subjects. Although there were variations across subjects, our approach provided time resolved data on the secretion of cytokines by cells, leading to the identification of specific signatures.
These REA antibodies are recombinant human IgGs that display negligible binding to Fc receptors. Stain specificity was verified with isotypes-matched control antibodies VioBright-FITC-conjugated IgG1, VioBlue-conjugated IgG1, APC-conjugated IgG1, PE-conjugated IgG1. Cells were incubated for 1 h at 4 °C in the dark, washed twice with PBS and analyzed by flow cytometry. After gating on CD3 lymphocytes, the CD3+ population was separated into CD4+ and CD8+ T cells. CD66+ neutrophils and CD14+ monocytes came from granulocyte and monocyte regions. All data were acquired using the flow cytometer LSRII (BD Biosciences) and analyzed with FlowJo software version 10 (FlowJo) and FACSDIVA software version 8. 0. 1 (BD Biosciences) to separate the different cell subsets constituting C. albicans -leucocyte cocultures.
t-distributed stochastic neighbor embedding analyses. After acquisition using flow cytometer LSRII, measured parameters were normalized and randomized. Data were first analyzed using FlowJo software. After doublets, debris, and dead cells excluding, living cells were gated. Downsampling function of multiple intraand interindividual measurements was applied before data concatenation. Resulting FCS files were analyzed with the tSNE module. Specific plots were generated by healthy subject in order to analyze the intra-individual phenotype of cell populations, and between subjects to modeling global immune responses to C. albicans. Specific analyses by C. albicans isolate was also realized. The following input settings were used before tSNE reduction: up to 300,000 concatenated cells per file, 1000 iterations, 20 perplexity, 0.
Statistics. Statistical analyses were all carried out with Prism V6. 0a software (GraphPad Software). The fungal burdens 6 days after challenge with each Candida isolate were compared using a two-way ANOVA test with Tukey's correction for multiple comparisons as previously described  32. For the quantification of intra-and inter-subject variability at baseline, we pooled data from all individuals and we used a data vector X = {x st } (individual s, time-point t) where each x st was associated with a subject and each subject had a total three measurements obtained from each of the time-points. Then we used the ANOVA model to evaluate the total variance (total sum of squares), subject-to-subject differences (between column variation) and the fraction of variance explained by subject relative to the residual of the fit.
05. The inter-subject variability was quantified within the columns as the sum of squares of differences between each subject and the sixteen subjects mean. The post-challenge variability was quantified as the sum of squares of the differences between the column means and the grand mean. The F ratio was the ratio of two sum of square values. Large F ratio signified higher post-challenge variability than inter-subject variability. The P value was determined from the F ratio and the two values of degrees of freedom. Immune cells frequencies data were analyzed according to UI controls, CI and PI outcomes. *P < 0. 05; **P < 0. 001; ***P < 0. 0001; ****P < 0. 00001; ns = not significant.
Introduction Association rule mining  [1]  is one of the most frequently used techniques to analyze customers' shopping behavior and derive actionable insights to enable Our work is funuded by a grant from TOTAL decision support. Like many others in the retail industry, marketers and product managers at TOTAL conduct regular studies of customer preferences and purchasing habits. The goal of those studies is to determine two main decisions: which products to bundle together in a promotional offer and which customers to target. Those studies usually focus on unveiling the interest of customers for specific products or categories (e. g. , tire service, gas, food) or the behavior of pre-defined customer segments. However, when the underlying dataset is extremely large, such as the one we use for our analysis from TOTAL (30 million receipts spanning 35 million records), it can create an explosion of association rules; therefore one has to make use of existing ranking measures of association rules, such as, Support, Confidence, Piatetsky-Shapiro, Lift, etc to rank the rules.
Step 3: We allow the domain expert non-scientists to provide feedback on the synthesized clusters. Step 4: We show how this process can provide actionable insights and enable decision support for virtually any customer segment and any product. Our analysis shows: rules that favor Confidence are best to determine which products to promote and rules that favor Recall are well-suited to find customer segments to target. To the best of our knowledge, this work is the first to run a large-scale empirical evaluation of insights on customer purchasing habits in the oil and gas retail domain. We summarize our contributions as follows : (i) a reproducible methodology for experimenting with different association rule ranking methods; (ii) several insights on real large-scale datasets; (iii) how to use association rules and interestingness measures in computing recommendations.
Empowering domain experts When analysts seek to determine which products to run a promotion for or which customers to target, they conduct small to medium-scale market analysis studies. Such studies are expensive, time-consuming and hardly reproducible. We use association rule mining to unveil valuable information about any customer segment and any product. Our collaboration with analysts at TOTAL resulted in the formalization of two kinds of purchasing patterns: those representing associations between a set of products and a single product (customers who wash their cars and purchase wipes also purchase a windshield washer), and those associating customer segments to a product category (young customers in the south of France who frequently wash their cars). Our dataset contains 30 million unique receipts, spanning 35 million records, generated at by 1 million customers at 3,463 gas stations , over three years (from January 2017 to December 2019).
In summary, this paper presents a joint effort between researchers in academia and analysts at TOTAL. We leverage the power of association rule mining and augment them with the power of rule ranking and summarization to guide decision support as well as the ability of performing product recommendation. The rest of the paper could be summarized as follows: The background and the goal of the work are provided in Section 2. Our underlying process using TOTAL datasets is described in Section 3. In Section 4, we describe how we summarize (cluster) interestingness measures based on similarities in rule rankings. These clusters are then evaluated by analysts in Section 5 leading to insightful findings. We discuss how to turn use our findings into product recommendation through association rule ranking in Section 6.
Dataset Our dataset represents customers purchasing products at different gas stations that are geographically distributed in France, for a period of two years (from January 2017 to December 2018). The dataset D is a set of records of the form t, c, p , where t is a unique receipt identifier, c is a customer, and p is a product purchased by c. The set of all receipt identifiers is denoted T. Each receipt identifier is associated with a unique customer, and multiple receipt identifiers can be associated with the same customer according to his/her visits to different gas stations. When a customer purchases multiple products in the same visit to a gas station, several records with the same receipt identifier t are generated. The complete dataset contains over 30 million unique receipts, spanning 35 million records, generated at 3, 463 gas stations, over three years.
The ratio 30/35 in our dataset is due to the fact that, unlike regular retail such as as shopping grocery stores  [12] , most customers at gas stations purchase gas only, and a few of them purchase additional products such as car services (oil change, car wash), drinks and food items. The set of customers, C, contains over 1 million unique records. Each customer has demographic attributes. In this study, we focus on 3 attributes: age, gender and location. The attribute age takes values in {< 35, 35 -49, 50 -65, > 65} and the attribute location admits French regions as values. We use demographics(c) to refer to the set of attribute values a customer c belongs to. For example, { < 35,F,Ile-de-France} represents a 28 years old female from the Ile-de-France region, whom we will refer to as Mary.
The attributes are used to form customer segments. Each segment is described by a set of user attribute values that are interpreted in the usual conjunctive manner. For example, the segment {< 35 , * , Ile-de-France} refers to young customers from the Ile-de-France region and the segment {> 65 , M, Normandie} refers to Senior Male customers from the Normandie region. The set of products P contains over 37, 556 entries, out of which 976 have been sold more than a thousand times. Each product p is associated with a product category. our dataset contains 54 different categories including gas, lubricants, car wash, hot drinks, and sweets. We use cat(p) to denote the category of a product p.
Mining Customer Receipts We describe our data preparation process -that is how to translate the sale receipts to a transactional dataset that could be further injected to the mining process. We then describe the mining scenarios and present interestingness measures to rank association rules. Dataset Preparation Figures 1, 2 and 3 report statistics on one month in the dataset which contains 407, 212 sales records generated by 257, 102 customers for 5, 479 products at 3, 079 gas stations. For confidentiality reasons, we do not report the statistics of the full dataset. We can however state that other periods in the dataset exhibit similar distributions. The statistics clearly show that the most purchased items are gas and that most transactions are short. To gain an understanding of customers' buying habits and provide them with relevant offers, analysts from TOTAL are interested in studying two kinds of purchasing patterns: those associating a set of products to a single product (customers who wash their cars and purchase wipes also purchase a windshield washer) and those representing associations between customer segments and a product category (young customers in the south of France who frequently wash their cars).
In all cases the analyst specifies a rule target B which corresponds to a product or a product category, and expects rules of the form A → B. In the first scenario, that we denote prod assoc, the analyst specifies a target product and is shown rules of the form set of products → target product, i. e. customers who purchase the set of products are likely to purchase the target product. In the second scenario, that we denote demo assoc, the analyst specifies a target category, and is shown rules of the form customer segment → target category, i. e. customers who belong to some segment are likely to purchase products in the target category. In both scenarios, the original dataset D is mapped into a collection of transactions T that is given as input to the mining process, as summarized in Table  2.
The set T is built differently according to each scenario. In the first scenario prod assoc, we generate the set of transactions T by grouping records in D by customer identifiers. For each customer c, we generate a single transaction containing the set of all products ever purchased by c {p| t, c, p ∈ D}. We obtain |C| transactions, each of which is a subset of P. This enables the discovery of customer patterns occurring over several visits to a station. The number of transactions in prod assoc is 1, 083, 901, where each transaction contains 7 products on average. In the second scenario demo assoc, a transaction is a tuple built for each record t, c, p by associating the customer segment demographics(c) with the corresponding category of the product cat(p). For example, an entry in the raw data consisting of the record 4523768, Mary, tea is mapped to the transaction Mining Scenarios Searching for regularities in a dataset plays an essential role in data mining tasks that retrieve interesting patterns.
Frequent itemset mining is the task of identifying sets of items which often occur together in the dataset. Given a frequency threshold ε ∈ [1, n], an itemset P is said to be frequent in a transactions set T iff support T (P ) ≥ ε where support T (P ) is the number of transactions in T that contain simultaneously all items in P. As indicated in Table  2 , we set the frequency threshold to 1, 000 in both scenarios. Because marketing actions are decided and applied nation-wide, they are expected to concern at least 1,000 customers. An itemset P is closed if and only if there exists no itemset P ⊃ P such that support T (P ) = support T (P )  [20]. The number of closed itemsets can be orders of magnitude less important than the number of itemsets, while providing   the same amount of information on T.
Acquisition and storage Each of the 3, 463 gas stations maintains a log of all customer transactions completed during one day. Whenever a customer authenticates her purchases using her loyalty card, a receipt containing the list of purchased products, their price, their category, as well as potential promotional offers, is generated. For each purchased product a record containing the receipt id, product id and customer id, is generated. These receipts are logged as r, c, p triples and stored in write-ahead log. Once a day, at closing time of each gas station, this log is transferred to the main data store. We have access to an SQL database containing the sales table where sales records are stored. Each customer is an entry in the customers table, which records the information she provided in her loyalty card (age, gender, region).
Data curation and preparation We first query the sales table to retrieve the full raw sales records. We also query the customers table to retrieve for each customer the corresponding segment attributes. At the end of this step, we generate two text files. Each line in the sales file is a triple r, c, p , and each line in the customers file is a quadruple c, age, gender, region. As described in Section 2. 2, mining customer receipts starts with the construction of a transactions dataset T according to the mining scenario specified by the analyst. We rely on Apache Spark and MapReduce operations to build the dataset T for each mining scenario. The sales file is loaded as a resilient distributed dataset. We maintain a HashMap that associates to each customer her segment, and another HashMap that associates to each product its corresponding category.
In the case of prod assoc, the products bought by a given customer are grouped by customer identifier using a groupByKey operation. In the case of demo assoc, a single map operation is sufficient. For each row r, c, p in the dataset, the map operation constructs a transaction age, gender, region, cat(p). In both cases, a dataset T is created as a text file, with one line per transaction. In prod assoc, an example of a line is gas, car wash, cafe, sandwich that represents all products ever purchased by a single customer. In demo assoc, an example of a line is > 65 , M , Ile-de-France, Soft drinks. Given a dataset T , we can now perform the mining process.