dataesr/scientific-paragraphs-categorization huggingface dataset:
Web Workspace EU-ADR Web Platform's key feature is the execution of advanced post-marketing adverse drug reaction studies. Researchers upload and investigate drug-event datasets, create targeted drug studies and work with their peers through the available collaboration features. Each researcher has its own personal workspace, where they can browse existing datasets (personal or shared); upload custom drug-event pair datasets; or create drug-specific datasets, based on the overall platform data. A researcher interested in studying potential adverse reactions of patients treated with a given drug, XYZ for the purpose of this discussion, begins its study by automatically generating a dataset focused on the targeted drug. The system then combines this drug with the 11 potential adverse events considered in EU-ADR's context, evaluates the resulting dataset using the available knowledge providers and combines all individual pieces of evidence into an aggregate score representing the predicted risk of each drug-event relationship -Figure  5.
The EU-ADR Web Platform is a unique tool that allows researchers to exploit the wealth of data from a European cohort, combined with independent drugevent datasets. In addition to being a step forward relative to existing solutions  [55] , the designed strategy accurately tackles multiple challenges behind the development of state-of-the-art software within the pharmacovigilance domain: scalability, interoperability, management, reproducibility, accessibility and security. • The plugin-based provider registry ensures that the platform is scalable. Where the standard defines the knowledge providers' interfaces, the provider registry stores metadata regarding the available algorithms, making them available as workflows for local or remote execution. • A new interoperability language was developed to ensure that all knowledge providers understand the data being exchanged, enabling accurate interactions within the distributed platform ecosystem.
• With knowledge providers managed through provider registry, collected data are stored in a cloud environment, streamlining the associated knowledge management tasks  [56]. • This proposal enables research reproducibility through the collection of multiple datasets, which include easily reproducible analysis results. This step is further improved through the use of a cloud-based knowledge base, storing all gathered and submitted data, and ensuring availability, reliability and an eased access for all the architecture components. • The platform's data analysis and exploration features are accessible through a web interface, constantly available to every researcher in any kind of system or device. • This new architecture enforces the establishment of secure communication channels amongst the platform and the knowledge providers, the security of datasets and the restricted web-based workspace.
Conclusion The goal of this paper is to verify the pertinence of an MDE approach in design and reconfiguration of a PLM system. With the metamodel proposed, we define a set of minimalist concepts for the control of conformity. With templates and transformation rules, we validated the feasability of a generation in an industrial system for SME/SMI. Currently constraints and transformation rules are not exhaustive especially for dynamic aspects of the model. Moreover, in the case of Audros, automated jobs can modify data depending on users events (for example: BOM creation following an event Project creation). These tasks are carried out by AUPL scripts (AUdros Programming Language). These can modify the data model or interact with it. To manage these various dependences and to maintain coherence a total of the system, Audros would need mechanisms to control the dependences (for creation and modification) between the various elements of its structure, as well as mechanisms of synchronization of scripts respecting the modifications of the model.
Acknowledgements This work was supported in part by  King Abdullah University of Science and Technology (KAUST) , the  European  project  ACROPOLIS-NoE  ( ICT-2009. 1. 1 ),the  COST Action IC0902 , and the  NPRP  grant  N 5-250-2-087  from the  Qatar National Research Fund  (a member of  Qatar Foundation ). The statements made herein are solely the responsibility of the authors. This work is an expanded version of work presented at the  International Conference on Cognitive Radio Oriented Wireless Networks  ( CROWNCOM 2012 ),  Stockholm, Sweden ,  June 2012.
The Online Encyclopedia of Integer Sequences (OEIS) is a catalog of integer sequences. We are particularly interested in the number of occurrences of N (n) of an integer n in the database. This number N (n) marks the importance of n and it varies noticeably from one number to another, and from one number to the next in a series. "Importance" can be mathematically objective (2 10 is an example of an "important" number in this sense) or as the result of a shared mathematical culture (10 9 is more important than 9 10 because we use a decimal notation). The concept of algorithmic complexity  [6, 2, 7]  (also known as Kolmogorov or Kolmogorov-Chaitin complexity) will be used to explain the curve shape as an "objective" measure. However, the observed curve does not conform to the curve predicted by an analysis based on algorithmic complexity because of a clear gap separating the distribution into two clouds of points.
Introduction Sloane's on-line encyclopedia of integer sequences  [10]   1  (OEIS) is a remarkable database of sequences of integer numbers, carried out methodically and with determination over forty years  [3]. As of August 29, 2012, the OEIS contained 215 260 integer sequences. Its compilation has involved hundreds of mathematicians, which confers it an air of homogeneity and apparently some general mathematical objectivity-something we will discuss later on. When plotting N (n) (the number of occurrences of an integer in the OEIS) two main features are evident: a. Statistical regression shows that the points N (n) cluster around k/n 1. 33 , where k = 2. 53 × 10 8. b. Visual inspection of the graph shows that actually there are two distinct sub-clusters (the upper one and the lower one) and there is a visible gap between them.
Thus, if we view OEIS in some sense as a universal Turing machine, algorithmic probability explains (a). Fact (b), however, is not predicted by algorithmic complexity and is not produced when a database is populated with automatically generated sequences. This gap is unexpected and requires an explanation. We speculate that OEIS is biased towards social preferences of mathematicians and their strong interest in certain sequences of integers (even numbers, primes, and so on). We quantified such a bias and provided statistical facts about it.
Presentation of the database The encyclopedia is represented as a catalogue of sequences of whole numbers and not as a list of numbers. However, the underlying vision of the work as well as its arrangement make it effectively a dictionary of numbers, with the capacity to determine the particular properties of a given integer as well as how many known properties a given integer possesses. A common use of OEIS is in determining the logic of a sequence of integers. If, for example, you submit to it the sequence 3, 4, 6, 8, 12, 14, 18, 20. , you will instantly find that it has to do with the sequence of prime augmented numbers, as follows: 2+1, 3+1, 5+1, 7+1, 11+1, 13+1, 17+1, 19+1. Even more interesting, perhaps, is the program's capacity to query the database about an isolated number. Let us take as an example the Hardy-Ramanujan number, 1729 (the smallest integer being the sum of two cubes of two different shapes).
OEIS comprises more than 200 000 sequences. A partial version retaining only the most important sequences of the database was published by Neil Sloane and Simon Plouffe  [11]  in 1995. It records a selection of 5 487 sequences  [11]  and echoes an earlier publication by Sloane  [9]. Approximately forty mathematicians constitute the "editorial committee" of the database, but any user may propose sequences. If approved, they are added to the database according to criteria of mathematical interest. Neil Sloane's flexibility is apparent in the ease with which he adds new sequences as they are proposed. A degree of filtering is inevitable to maintain the quality of the database. Further, there exist a large number of infinite families of sequences (all the sequences of the form (kn), all the sequences of the form (k n ), etc.
), of which it is understood that only the first numbers are recorded in the encyclopedia. A program is also used in the event of a failure of a direct query which allows sequences of families that are not explicitly recorded in the encyclopedia to be recognized. Each sequence recorded in the database appears in the form of its first terms. The size of first terms associated with each sequence is limited to approximately 180 digits. As a result, even if the sequence is easy to calculate, only its first terms will be expressed. Next to the first terms and extending from the beginning of the sequence, the encyclopedia proposes all sorts of other data about the sequence, e. g. , the definitions of it and bibliographical references. OEIS is available in the form of a data file that is easy to read, and that contains only the terms retained for each sequence.
The sequence (N (n)) n∈N * is generally characterized by a decreasing curve. However, certain numbers n contradict this rule and possess more properties than their predecessors: N (n) > N (n -1). We can designate such numbers as interesting. The first interesting number according to this definition is 15, because N (15) = 34 183 and N (14) = 32 487. Appearing next in order are 16, 23, 24, 27, 28, 30, 35, 36, 39, 40, 42, 45, 47, 48, 52, 53, etc. We insist on the fact that, although unquestionably dependent on certain individual decisions made by those who participate in building the sequence database, the database is not in itself arbitrary. The number of contributors is very large, and the idea that the database represents an objective view (or at least an intersubjective view) of the numeric world could be defended on the grounds that it comprises the independent view of each person who contributes to it and reflects a stable mathematical (or cultural) reality.
Overview of the theory of algorithmic complexity Save in a few exceptional cases, for a number to possess a multitude of properties implies that the said properties are simple, where simple is taken to mean "what may be expressed in a few words". Conversely, if a number possesses a simple property, then it will possess many properties. For example, if n is a multiple of 3, then n will be a even multiple of 3 or a odd multiple of 3. Being a "even multiple of 3" or "odd multiple of 3" is a little more complex than just being a "multiple of 3", but it is still simple enough, and one may further propose that many sequences in Sloane's database are actually sub-sequences of other, simpler ones. In specifying a simple property, its definition becomes more complex (by generating a subsequence of itself), but since there are many ways to specify a simple property, any number that possesses a simple property necessarily possesses numerous properties that are also simple.
Given that K(n) is non computable because of the undecidability of the halting problem and the role of the additive constants involved, a precise calculation of the expected value of N (n) is impossible. By contrast, the strong analogy between the theoretical situation envisaged by algorithmic complexity and the situation one finds when one examines N (n) inferred from Sloane's database, leads one to think that N (n) should be asymptotically dependent on 1/2 K(n). Certain properties of K(n) are obliquely independent of the reference language chosen to define K. The most important of these are: • K(n) < log 2 (n) + 2 log 2 (log 2 (n)) + c (c a constant) • the proportion of n of a given length (when written in binary) for which K(n) recedes from log 2 (n) decreases exponentially (precisely speaking, less than an integer among 2 q of length k, has an algorithmic complexity K(n) ≤ k -q).
2. If i = 1, f is defined by choosing uniformly at random a constant k ∈ {1,. , 9}, a binary operator ϕ from among the following list: +, ×, and subtraction sign, in a uniform manner, and a unary operand g that is identity with probability 0. 8, and the function squared with probability. 2 (to reproduce the proportions observed in Sloane's database). One therefore posits f i (n) = ϕ(g(n), k). 3. If i ≥ 2, f i is defined by f i (n) = ϕ(g(f i-1 (n)), k), where k is a random integer found between 1 and 9, g and ϕ are selected as described in the point 2 (above), and f i-1 is a random function selected in the same manner as in 2. For each function f that is generated in this way, one calculates f (n) for n = 1,. , 20. These terms are regrouped and counted as for N (n). The results appear in Figure  4.
The gap: A social effect? This anomaly with respect to the theoretical implications and modeling is undoubtedly a sign that what one sees in Sloane's database is not a simple objective measure of complexity (or of intrinsic mathematical interest), but rather a trait of psychological or social origin that mars its pure expression. That is the hypothesis that we propose here. Under all circumstances, a purely mathematical vision based on algorithmic complexity would encounter an obstacle here, and the social hypothesis is both simple and natural owing to the fact that Sloane's database, while it is entirely "objective", is also a social construct. Figure  5  illustrates and specifies our hypothesis that the mathematical community is particularly interested in certain numbers of moderate or weak complexity (in the central zone or on the right side of the distribution), and this interest creates a shift toward the right-hand side of one part of the distribution (schematized here by the grey arrow).
In  [15] , new arrival data along with the data violating the KKT conditions, and the support vectors from the last iteration, were considered as a new training dataset to train the classifier at the current step. The iteration will be stopped when all data satisfy the KKT conditions. In  [16, 17] , the authors analyzed the change of the KKT conditions when one datum was included into, or removed from, the training set; then, a so-called bookkeeping step was used to compute the new coefficients of the classifier to achieve an online update for a two-class SVM. Useful implementation issues on incremental SVM were presented in  [18]. In  [7] , it was argued that the binary classification algorithm in  [16]  cannot be directly implemented for a one-class problem. In  [7, 19] , the authors considered the change of the normal model over time and online identified outliers using previous data vectors in a sliding time window.
This step can be generalized as: C D = {C 1 , C 2 ,. , C m }, 1 ≤ m ≪ n offline ---→ coefficient matrix K β -ρ ⊤ ( 56 ) Step 3-sparse: After learning the initial dictionary, C D , including the first m (1 ≤ m ≪ n) samples, the remaining training samples, {C m+1 , C m+2 ,. , C n }, are learned via sparse online LS-OC-SVM described in Section 3. 4. This step can be described in the following equations: {C D , C k }, m < k ≤ n sparse online -------→ coefficient matrix β -ρ ⊤    C D := C D ∪ C k if ǫ t ≥ µ 0 C D := C D if ǫ t < µ 0 (57) where C D is the dictionary and C k is a new incoming remaining sample in the training dataset. According to the coherence criterion introduced in Section 3. 4, if the new sample, C k , satisfies the dictionary updated condition, it will be included into the dictionary, C D.
Synthetic Dataset via Online LS-OC-SVM and Sparse Online LS-OC-SVM Two synthetic data, "square" and "ring-line-square"  [48] , are used. The "square" consists of four lines, 2. 2 in length and 0. 2 in width. In the area of these lines, 400 points were randomly dispersed with a uniform distribution. The "ring-line-square" distribution is composed of three parts: a ring with an inner diameter of 1. 0 and an outer diameter of 2. 0, a line of 1. 6 in length and 0. 2 in width, and a square the same as dataset "square" introduced above. 850 points are randomly dispersed with a uniform distribution. These two data are shown in Figure  4. The first sample is used for initializing the online LS-OC-SVM proposed in Section 3. 3; the 399 remaining samples in "square" and 849 remaining samples in "ring-ling-square" are learned in the online manner.
Abnormal Visual Event Detection via Online LS-OC-SVM UMN dataset detection results via online LS-OC-SVM proposed in Section 3. 3 are shown below. The UMN dataset consists of eleven sequences of crowded panic escape events, which are recorded in a lawn, an indoor and a plaza scene. A frame where the people are walking in different directions is considered as a normal sample for training or for normal testing. A scene where the people are running is taken as an abnormal sample for testing. The detection results of the lawn scene, the indoor scene and the plaza scene are shown in Figures 7, Figure  8  and Figure  9 , respectively. A Gaussian kernel for the covariance matrix in the Lie group is used. Various values of the variance, σ, in the Gaussian function and the penalty factor, C, are chosen to form the receiver operating characteristic (ROC) curve.
Abnormal Visual Event Detection via Sparse Online LS-OC-SVM UMN dataset abnormal event detection results via sparse online LS-OC-SVM proposed in Section 3. 4 are presented. Taking the lawn scene as an example, the first normal covariance matrix descriptor from the training samples is included into the dictionary firstly; then, the remaining training covariance descriptors are learned online by the sparse online LS-OC-SVM method. The ROC curve of the detection results of the lawn scene, the indoor scene and the plaza scene are shown in Figure  10a-c , respectively. The resulting performances when all training samples are learned offline via one-class SVM (OC-SVM), learned via least squares one-class SVM (LS-OC-SVM), learned via online least squares one-class SVM (online LS-OC-SVM) and learned via sparse online least squares one-class SVM (sparse LS-OC-SVM), are shown in Table  2.
However, the computation of the HOF takes more time than the computation of covariance. By adopting the integral image  [43] , the covariance matrix descriptor of the subimage can be computed conveniently. The covariance descriptor can appropriately be used to analyze partial image movement. In  [49] , the whole training dataset was saved in the memory in advance; then, the dictionary was chosen as an optimal subset for reconstructing. Our sparse online LS-OC-SVM strategy enables one to train the classifier with sequential inputs. This property makes our proposed method extremely suitable to handle large volumes of training data, while the method in  [49]  fails to work due to lack of memory. Table  3. The comparison of our proposed method with state-of-the-art methods for abnormal event detection in the UMN dataset.
Conclusions In this paper, we proposed a method to detect abnormal events via online least squares one-class SVM (online LS-OC-SVM) and sparse online least squares one-class SVM (sparse online LS-OC-SVM). Online LS-OC-SVM learns training samples sequentially; sparse online LS-OC-SVM incorporates the coherence criterion to form the dictionary for a sparse representation of the detector. The covariance matrix descriptor encodes the movement feature of the frame to distinguish between normal and abnormal events. The proposed detection algorithms have been tested on a synthetic dataset and a real-world video dataset yielding successful results in detecting abnormal events.
Acknowledgments This work is partially supported by the  China Scholarship Council of Chinese Government  and the  SURECAP CPER project (fonction de surveillance dans les réseaux de capteurs sans fil via contrat de plan Etat-Région ) and the  Platform CAPSEC  (capteurs pour la sécurité) funded by  Région Champagne-Ardenne  and  FEDER  (fonds européen de développement régional).
Multiway Wiener filter in multidimensional wavelet packet domain After the MWPT, abundant and rare signals can be separated into different components; therefore, the signal subspace of each component can be estimated more accurately than that of the entire dataset. Furthermore, a better estimation of the signal subspace can improve the performance of MWF in each component. However, the denoising criterion of MWPT-MWF is the minimization of the MSE between X and X but not the component and its estimate; therefore, this subsection proves the ability of MWPT-MWF in minimizing the MSE between X and X , which is defined in  (21). By performing MWPT to tensor R, X and N in expression (4), we obtain R × 1 W 1 × 2 W 2 × 3 W 3 = (X + N ) × 1 W 1 × 2 W 2 × 3 W 3 = X × 1 W 1 × 2 W 2 × 3 W 3 + N × 1 W 1 × 2 W 2 × 3 W 3.
Clinical monitoring and laboratory follow up At each visit (Study-T: days 0, 1, 2, 7, 28; Study-E: days 0, 1, 2, 3, 7, 14, 21, 28, 35, 42) a standardized symptoms questionnaire and physical examination were conducted. A β-human chorionic gonadotropin (βHCG) urine-pregnancy test was done on day 0 and 28 for females ≥12 years. In Study-T, malaria blood smears were obligatory on days 0, 2 and 28. In Study-E blood smears were done on days 0, 2, 3, 7, 14, 21, 28, 35, 42. On day 0 a serum sample (from venous blood) was stored at -20°C for further analyses. The routine assessment of blood levels of liver enzymes aspartate aminotransferase (AST) and alanine aminotransferase (ALT) was implemented after the start of the tolerability trial (Study-T), following the recommendation of the data safety monitoring committee.
Sample size Study-T: one thousand participants allowed to detect a minimum of 2. 7-3. 3% difference between relatively low AE frequencies (1-2% in one study arm, versus 3. 7-5. 3% in the other study arm), and a minimum of 8-8. 4% differences for relatively high AE frequencies (25-30% in one study arm versus 38. 4% in the second arm (80% power and 0. 05 significance level) (Power Analysis and Sample Size calculation Software for Windows, 2007, NCSS, Utah, USA). The Study-E sample size was powered to assess the non-inferiority of ASAQ versus AL treatment efficacy  [13].
Discussion The tolerability profile of two widely used fixed-dose ACTs, ASAQ and AL, for the treatment of uncomplicated P. falciparum malaria, was assessed in a trial of 1,000 patients over five years old, an age group not frequently studied in the past and able to express symptoms. The findings were complemented with safety data collected in a parallel trial from 300 children under five years. In both studies, ASAQ and AL were very well tolerated and highly efficacious (>90% genotyping-adjusted cure rates,  [13]. Clinical-or laboratory adverse events (AEs) reported in both studies corresponded closely to abnormalities seen in malaria infection itself, and were overall consistent with recent findings. Severe AEs were few and mostly laboratory abnormalities, which did not manifest clinically.
Acknowledgements We thank the study participants and the guardians of participating children. We are grateful for the support of  Médecins sans Frontières teams in Switzerland, Geneva and in Monrovia and Nimba County, Liberia. We acknowledge the study team, including physician assistants  Colee R Marnia ,  Jnr ,  Tarnue Jallah ,  Mohamed K Mansalay , laboratory technicians  V Yatta Walker ,  Andrew Samorlu ,  Mathilda Clinton ,  Tobias H Johnson ,  Mathew G Kwaidah ,  Linda Nyamedor , nurses  Felicia George ,  Suwulubalah Molubah Dorborson ,  Masu Sasay ,  Wannie Wesley ,  Oretha Page ,  Rachel G Fannoh ,  Nyama K Loila , nurse aids  Kononon Karduan ,  Roosevelt Suomie ,  Margarethe Scere ,  Saretta F Glee ,  Lois M Dolo , and data clerks  Chabe Jobe  and  Isaac Williams. We also thank the  CHC  staff.