dataesr/scientific-paragraphs-categorization huggingface dataset:
Study data and variables of interest All the participating centres reported the patients' information using consistent electronic case report forms (eCRF). Collected demographic information included age, sex, height, weight, and BMI. Furthermore, information about the presence of symptoms before hospitalization and the duration of hospital stays before ICU admission were also recorded. Sub-scores of sequential organ failure assessment (SOFA): respiratory, cardiovascular, hepatic, coagulation, renal, and neurological systems were calculated at the ICU admission. Six different pre-existing comorbidities were also documented in eCRF form: diabetes, ischemic heart disease, renal insufficiency, arterial hypertension, pulmonary comorbidity, and congestive heart failure. The definitions of these comorbidities are available in S1 Text.
Several laboratory measurements were also retrieved for the patients during their ICU admission. The partial pressure of oxygen (PaO2) and the fraction of inspired oxygen (FiO2) were recorded based on the first arterial blood gas (ABG) analysis. The highest measured values of serum bilirubin, serum creatinine, c-reactive protein, and leukocytes count were documented on admission day. The serum lactate concentration was reported on both the first and second days of ICU admission separately. Furthermore, the minimum available measurements of thrombocyte count and lymphocyte count were also recorded. Information on drug therapy during patients' ICU stays included antibiotics, corticosteroids, and antiviral drugs, while also documenting bacterial co-infection. Finally, therapeutic interventions including invasive and non-invasive ventilation, vasopressor use, renal replacement therapy, tracheostomy, as well as their day of occurrence after ICU admission were also available in the dataset.
Data preparation After removing inconsistently recorded information, we standardised variables into unique measurement units. For example, we converted PaO2 values with kilopascals (kPa) to millimetres of mercury (mmHg). Then we transformed ICU length of stay, mortality time and intervention start time into daily units. We converted textual notes of drug usage into binary variables indicating whether a drug was administered. We also extracted the SOFA sub-scores for each patient from their textual clinical notes and converted them to 6 separate variables with values ranging from 0 to 4. However, in the case of an incorrectly reported text, they were considered as missing values. We also checked the total SOFA score for consistency, summing all the sub-scores. To improve interpretability, we defined four clinically meaningful intervals for each intervention, namely 0 to 2 days, 3 to 6 days, 7 to 30 days, and after 30 days, which then became separate binary variables.
Furthermore, to mitigate the effect of noisy data and outliers in the dataset, we defined clinically valid intervals for the relevant variables and those variables outside of the intervals were considered as missing values. Since machine learning models typically cannot handle missing data, we imputed the missing information of patients in the dataset in several steps. We considered unreported values of bacterial co-infections and those reported as "unknown" as missing values. Then, we used the median for continuous and mode for categorical variables to impute the missing values in the rest of the dataset. Finally, we transformed each continuous variable individually within a zero to one range, maintaining their distribution, while we encoded the rest of the categorical variables using the one-hot encoding scheme.
Model development and validation During model development we compared the performance of three algorithms, namely, Extreme Gradient Boosting (XGBoost)  [17]  as the primary model with Feed-Forward (FF) neural network and Logistic Regression (LR)  [18]  to predict ICU mortality, 30-days mortality after ICU admission, and low-risk patients admitted to the ICU. XGBoost is an ensemble of decision trees that provides robust predictive performance with learning complex and non-linear relationships in data using an ensemble learning technique called boosting. Boosting is an iterative learning process, sequentially building many models that correct the deficiencies of the preceding model. Even though deep neural networks provide better predictive performance in unstructured datasets, XGBoost has shown great predictive performance for structured, tabular data  [19].
Study population The overall dataset contained 3,933 electronic health records of patients, out of which 3,474 patients remained after applying the exclusion criteria as shown in the cohort selection diagram in S8 Text. The final cohort contained patients originating from 37 different countries (17 European and 20 non-European), admitted to ICUs between January 11, 2020, and April 27, 2021, shown in Fig  2. The European cohort included 2,858 patients with an average mortality rate of 45% both in ICU and 30 days after ICU admission, while 13% of patients were at low risk of deterioration. European patients' age median was 75 years (IQR, [72-78]), with 30% female, and median length of ICU stay was 13 days (IQR,  [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] ).
The distribution of patients among the European countries, including the number of patients as well as ICU mortality rate per country is shown in France was chosen as the validation cohort to assess the generalisability among the European cohort because it had the highest number of patients in the database (647, or 22% of the European cohort), with 40% mortality rate (ICU and 30-day) and 19% of patients with a low risk of deterioration. Furthermore, we also evaluated generalisability of the European predictive model on a per-country basis using leave one country out approach. Namely, we selected nine European countries with the highest number of ICU admissions and separately evaluated each corresponding cohort on the model derived from the remaining European countries as shown in S6 Text. We prospectively evaluated temporal generalisability of our European model using a cohort of 715 (25%) patients admitted to ICU after December 1st, 2020, with a median age of 75 years Mortality rate in the ICU and 30-day for the derivation cohort (up to cut-off date of December 1 st , 2020) was 41% and 42% respectively.
As can be seen from Fig 10 patients that did not survive had Renal Replacement Therapy administered more often during the first week of stay (bottom graph, dark blue) than the patients that survived (top graph, light blue). Furthermore, for patients that survived, tracheostomy was administered more often in comparison to patients that did not survive. In terms of the other types of interventions, namely mechanical and noninvasive ventilation as well as vasopressors, we did not find significant differences in our dataset.
Limitations The present study has some methodological limitations in common with the other COVIPstudies  [29] [30] [31] [32] [33] , such as COVIP does not contain a control group of younger COVID-19 patients for comparison, or a comparable age cohort of ICU / non-ICU patients. In addition, the COVIP database does record information on time from symptoms onset to ICU admission. These treatment limitations might also affect the care of older ICU patients. Furthermore, COVIP recruited patients in many countries with a wide variety in their care structure, resulting in a considerable heterogeneity of treatments.
Funding: The study was supported by a grant from  Fondation Assistance Publique-Hopitaux de Paris  supported this study. EOSCsecretariat. euprovided support and has received funding from the  European Union's Horizon Programme  (grant agreement number: 831644); ( H2020-INFRAEOSC-05-2018-2019 ). This work was supported by the  Forschungskommission of the Medical Faculty of the Heinrich-Heine-University Du ¨sseldorf , No. 2018-32  to G. W. and No. 2020-21  to RRB for a Clinician Scientist Track. The funders had no role in this work. grants from  Orion Pharma ,  Abbott Nutrition International, B. Braun Medical AG, CSEM AG, Edwards.
More and more organizations are under cyberattacks. To prevent this kind of threats, it is essential to detect them upstream by highlighting abnormal activities within networks. This paper presents our anomalies detection approach that consists of aggregating pre-processed network flows into sectors. Then for each sector, data are split into equal time periods. Finally an unsupervised clustering algorithm is employed to extract that we called activity deviations. If a specific sector network activity for one specific period differs from others, it means that a network anomaly has been detected. Our experiments are based on a real dataset provided by a French mobile operator. With our proposed method, we have been able to detect anomalies corresponding to real crowded events like fire, soccer match or concert.
Some researches have been done on this topic by monitoring network traffic and analysing message headers or payload content  [2]. Countermeasures mainly consist of analysing traffic flows by deploying network probes like Intrusion or Anomaly Detection Systems  [3]. Anomaly detection is useful in identifying the patterns that are unexpected called outliers. Moreover, Machine Learning (ML) techniques are increasingly used to detect them. Several works are available in the literature. For example, authors in  [4]  proposed a ML algorithm for classifying heterogeneous network traffic and anomaly detection. Also, a recognition algorithm was used in order to validate the model. On the other side, authors in  [5]  proposed a solution based on both deep residual Conventional Neural Network (CNN) structure for dataset modelling and transfer learning for training the model to detect anomalies in Industrial Control Systems (ICS).
ML techniques can also be used to detect outliers in sensor data. Authors in  [6]  described several techniques of ML to detect outliers in IoT by classifying the existing techniques into different categories. For our use case, we aim to detect behaviour changes relating to mobile application activity using an unsupervised ML technique and based on the fields described in Section III. To achieve this, we extracted that we call activity deviations by analysing network activity behaviours at a specific moment in time and for a particular network location. This article presents our network anomalies detection approach using machine learning to extract deviations and detect outliers. Our paper is organized as follows. In Section II, we explain our proposed method. Section III describes the dataset that we used for performing our research that is based on real mobile network data and the provided raw flows.
This is a public domain geocode system invented in 2008 by Gustavo Niemeyer which uses GPS coordinates to encode a geographic location into a short string of letters and digits  [8]. It is a hierarchical spatial data structure which subdivides space into buckets of grid shape. The sector area depends on the 'Geohash length' also known as Precision. The higher the Geohash precision value is, the smaller its corresponding sector area. Table I lists some cells dimensions depending on Geohash precision. To determine the most accurate precision value, different Geohash lengths from 5 to 7 characters were tested. A 6-character long Geohash has been selected because its precision fits the best understandable well-known places to study like Notre-Dame de Paris (N-d-P) or Stade de France (S-d-F) as depicted by Figs.
III. EMPLOYED DATASET For this study, we have employed a dataset that has been generated to serve several research objectives [?]: (i) collecting new measurements that describe mobile network data traffic for different mobile services separately. Data are gathered in an operational nationwide network by combining the output of multiple monitoring technologies (ii) evaluating existing analytics for classification, prediction and anomaly detection within real-world high-detail per-service mobile network data that represents several terabytes of traffic per hour (iii) demonstrating the integration of data analytics within next-generation network architectures in practical case studies. This dataset consists of data collected from a major French mobile operator at large scale. It is made of novel anonymized high-detail flows of contextualized user mobility and mobile service usage traffic data records (xDR).
Network flows have been obtained by leveraging passive probes deployed in the French operator mobile network. These data sources have different levels of spatio-temporal accuracy. It characterizes the mobile traffic and application usage over a 3-month period for the entire national territory of France. Data has been erased according to GDPR rules and the national telecom code of conduct but anonymized aggregates are available for analyses. This paper focuses on the Ile-de-France area to detect anomalies in a mobile network based on recurring behaviour analysis at an application level. The dataset employed in this study is composed of 26 '.
0G 154M df U 681M 38M df J 1. 5G 88M df V 13G 814M df K 881M 53M df W 211M 8. 7M df L 18G 1. 1G df Y/Z 70M/316K 5. 4M/128K By-site aggregation details -Ratio ≈ 16 Fig. 4. By-sector aggregated data snippet  It has been implemented using Weka  [14]. We employed the Waikato Environment for Knowledge Analysis Toolbox  [15]  to conduct our experiments. It is a freely available open source software developed at the University of Waikato in New Zealand providing a set of visualization tools, different machine learning algorithms for data analysis and extensions, together with graphical user interfaces for ease of access to these functions. Overall, data must be transformed into an ASCII text file that describes a list of instances sharing a set of attributes to be analysed with Weka. This step is done by the parsers during the aggregation processes.
V. CONCLUSION & FURTHER WORKS From this study, we can conclude that our proposed process based on an Unsupervised Activity Deviations Extraction is able to detect network anomalies. In this use case, anomalies are defined by a behaviour change in network activity. We selected this kind of behaviour because application activity was a feature available in our dataset but it can be transposed over other networks with different parameters. Our approach consists in the first place to aggregate data into sectors that represent specific areas. Then, an unsupervised MLA is employed to extract network behaviours from these sectors. Based on those features, we characterize some activity deviations for each sector and for particular time periods. After that, we extracted outliers to detect any network anomalies.
I. INTRODUCTION Physically Unclonable Functions (PUFs) are considered nowadays as one of the emerging security primitives for resource-constraint ecosystems in the field of IoT  [1]. PUFs are pervasively used to generate device-specific data which can be used in several applications such as light-weight device authentication, and encryption key generation  [2]. PUF is characterized as a hardware bound function which utilizes the unit-specific micro-variations to generate device-specific data. The functionality of PUF is based on mapping a bit-vector challenge (the input) to a response (output) and generate a so called Challenge-Response-Pair (CRP). Variations of PUF structure exist, which differ in how the PUF functions and how many device-specific data the PUF can generate. Strong PUF is a macro variant which aims at generating an abundance of device-specific identifiers.
Very large number of CRPs can be generated by an strong PUF depending on This material is based upon the work supported by the French National Research Agency in the framework of the "Investissements d'avenir" program (ANR-15-IDEX-02). the size of the challenge, so called the input dimensiality of the PUF  [3]. This characteristic in turn makes strong PUF an ideal primitive for cryptographic applications such as singleuse (one-time pad; OTP) key generation  [4] ,  [5]. Usually strong PUF is enrolled via a database of CRPs captured from the PUF circuit before deployment  [2] ,  [4]. However, several authentication and key generation protocols exist which suggest enrolling PUF with its software model equivalent, or an estimation model which can identify the PUF  [6] -  [9]. In the primary approach, a software model is the stored data on the verifier server to provide access to the full CRP space of the enrolled strong PUF.
This in turn solves the shortage of CRPs on the verifier server, eliminating the requirement to re-enroll and thus restock on the CRP database. It also can be considered as a compact solution for enrolling the PUF, therefore, requiring less memory space on the storage device of the verifier server  [9]. The common approach in building the equivalent software model of PUF is to use Machine Learning (ML) modeling techniques. The idea is to train a probabilistic model which can estimate the CRP characteristic of the PUF with high probability. In this approach, a set of CRPs is captured and a training algorithm is used to converge the model's characteristic to an estimation of the target PUF's CRP characteristic, according to the captured CRP set  [10] ,  [11]. An important challenge in ML-based modeling of PUF is to deal with the structural complexity of the PUF (for instance k-XOR Arbiter PUF with k larger than 4).
B. Strong PUF Enrollment with ML The goal of PUF enrollment with ML-based modeling is to replace the conventional CRP database with an estimated model of the PUF. Let us denote the estimated model of PUF as h P U F. The enrollment of PUF with ML-based modeling means that the verifier server will own h P U F which provides access to the full CRP space of the PUF circuit with some miss-prediction error which is tolerable. During the enrollment the server has open access to the PUF CRPs to build the h P U F (see Fig. 2 (a) ). Let us consider c i as a challenge input to the PUF circuit. If we observe the PUF circuit as a function f P U F of c i , then it's estimation can be defined as a function g P U F of c i and a set of internal values θ of the model. thus h P U F = {g P U F , θ}. The estimation should then follow (1): f P U F (c i ) = r i ≈ r i = g P U F (c i , θ) = h P U F (c i ) (1) Where r i is the PUF circuit's response to the challenge c i and r i is the estimated model's prediction of r i for c i.
Experimental setup 2. 2. 1. Portable X-ray fluorescence spectroscopy As the ultimate objective of this study is effective carbonate identification and quantification in the field, all the elementary analyses were undertaken using LIBS and XRF portable devices. Portable XRF data were performed with a Thermo Fisher Scientific Niton XL3t GOLDD+ handheld spectrometer. The instrument uses an Ag-anode X-ray tube with a voltage of 50 kV and is equipped with a silicon drift detector (SDD). It operates the Main, Low, High, and Light filters with a duration of 30 s for each filter. The spectral data was automatically interpreted by the Mining Cu/Zn mode, which is effective for the measurement of lighter elements such as Mg, Al, Si, P, and S. In order to obtain a statistically reliable result, the elemental concentrations of each sample were averaged from 5 measurements at the same point with an 8 mm diameter for an analytical window.
The candidate lines commonly used in previous studies with respect to Ca, Mg, Fe, and Mn are listed in Table  2. This method requires assigning each peak of the LIBS spectra to the corresponding element in agreement with the NIST or AtomTrace databases. In the case of Ca, two emission-line candidates of calcium for carbonates in previous studies and three newly confirmed lines in this study were compared to the changes in calcium concentration  [21, 24]. Univariate calibration curves for concentration measurement are established by comparing the LIBS signals, such as peak intensity or area, on the vertical axis with concentration values of the analyte on the horizontal axis. Nonlinear calibration curves are inappropriate for concentration estimation because signal changes according to the increase in elemental concentration are constant at higher concentrations.
Quantification of major element contents 3. 1. 1. Error calculation To work towards understanding the relationship between the elemental concentrations and between the peak intensity or area, the determination coefficient (R 2 ), which is a simple and common univariate statistical model, was given by: R 2 = 1 - ∑ N i=1 (ŷ i -y i ) 2 ∑ N i=1 (ŷ i -ȳ i ) 2 (1) where ŷi represents the reference values of concentration, y i the predicted ones, and ȳ the average value of ŷi over N samples in the dataset. This regression formulation only takes into account observational errors in the dependent variable and could therefore be effective for the calibration dataset and for cross-validation. However, since the R 2 is definitely not sufficient to assess the predictive ability of a model, other model assessments were necessary to support the estimation of accuracy for a quantitative method.
Principal component analysis (PCA) Principal component analysis (PCA) is one of the most widely used chemometric procedures for multivariate data systems. It reduces the dimensions of the input data to reveal the simple underlying structure that provides an adequate visual representation of that data. PCA of the LIBS spectra was carried out using FactoMineR (Factor Analysis and Data Mining with R) package in the R platform  [23, 26, 36]. To systematically investigate the chemometric capability for carbonate discrimination, four different datasets were used: i) all the signals from the entire LIBS spectra, ii) the total area calculated from the region of interest of LIBS spectra for the four selected emission lines used in this study, iii) the net area of each selected emission line, and iv) LIBS total area and the concentration of the four cations obtained from pXRF (Fig.
14 ). Based on our results above, we further carried out PCA by adding the binary carbonate mixtures among calcite, ankerite, and dolomite with the LIBS dataset i), ii), and iii). All the datasets were applied baseline correction and normalization to the total area of the spectrum processed by Spectragryph v1. 2. 15  [25]. The entire spectrum corresponds to the LIBS broadband spectra, which consists of all the intensities in the whole wavelength region of the detector from 180 to 960 nm. The data for the entire spectrum projected on the PC1 vs. PC2 plane account for 72. 58% of the variance. The PCA plot shows that rhodochrosite, siderite, and magnesite are well classified and scattered on the PCA plot, while calcite, dolomite, and ankerite are clustered closely together (Fig. 14A ). In the ankeritedolomite solid solution, one dolomite sample (Fe#: 0.
024) is plotted between another dolomite sample (Fe#: 0. 017) and the ankerite sample (Fe#: 0. 23) instead of being separated from each carbonate group (Fig. 14A ). This result shows that the dataset consisting of all the LIBS signals from the entire region is the simplest to apply to PCA compared to the other datasets since it is directly applicable after exporting and correcting the LIBS data. However, we also confirmed that this dataset required much time consumption according to many variables (i. e. , 8262 variables) and difficulty in distinguishing among calcite, dolomite, and ankerite from the PCA plot. The PCA plot consisting of the total area calculated from the region of interest of four emission lines shows the best classification for carbonate discrimination, exhibiting 74. 81% for the sum of the PC1 and PC2 (Fig.