dataesr/scientific-paragraphs-categorization huggingface dataset:
In  [5] , the authors established a neural network with graph-based attention model that exploits ancestors extracted from the OWL-SKOS representations of ICD Disease, Clinical Classifications Software (CCS) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT). In order to exploit the hierarchical concepts of these knowledge graphs in their attention mechanism, the graphs are transformed using the embedding obtained with Glove  [15]. The results show that such a model better performs when identifying a pathology rarely observed in a training dataset than a recurrent neural network and it also better generalizes when confronted with less data in the training set. Again, this work also does not exploit other kinds of relationships, while we compare the impact of different kinds and sources of knowledge.
In  [16]  the authors extract knowledge from the dataset of  [13]  and structure it with an ontology developed for this purpose, then they automatically deduce new class expressions, with the objective of extracting their attributes to recognize activities of daily living using machine learning algorithms. The authors highlight better accuracy and results than with traditional approaches, regardless of the machine learning algorithm on which this task has been addressed (up to 1. 9% on average). Although they exploit solely the ontology developed specifically for the purpose of discovering new rules, without trying to exploit other knowledge sources where a mapping could have been done, their study shows the value of structured knowledge in classification tasks. We intend here to study the same kind of impact but with different knowledge sources and for the task of predicting hospitalization.
Extraction of Ontological Knowledge from EMRs Our study aims to analyze and compare the impact of knowledge from different sources, whether separately incorporated or combined, on the vector representation of patients' medical records to predict hospitalization. To extract domain knowledge underlying terms used in text descriptions written by general practitioners, we search the texts for medical entities and link them to the concepts to which they correspond in Wikidata, DBpedia and health sector specific knowledge graphs such as those related to drugs. Wikidata and DBpedia were chosen because general concepts can only be identified with general repositories. In this section, we describe how these extractions are performed but we do not focus on this step as it is only a means to an end for our study and could be replaced by other approaches.
Knowledge Extraction based on Wikidata Wikidata 5  is an open knowledge base that centralizes data from various projects of the Wikimedia Foundation. Its coverage on some domains differs from that of DBpedia. We extracted drug-related knowledge by querying Wikidata's endpoint. 6 More precisely, we identified three properties of drugs relevant to the prediction of hospitalization: 'subject has role' (property wdt:P2868), 'significant drug interaction' (property wdt:P2175), and 'medical condition treated' (property wdt:P769). In Wikidata, we identify the drugs present in EMRs using the ATC code (property wdt:P267) of the drugs present in the PRIMEGE database. The CUI UMLS codes (property wdt:P2892) and CUI RxNorm (property wdt:P3345) have been recovered using medical domain specific ontologies.
We thus extracted knowledge based on three OWL representations specific to the medical domain: ATC, 7  , NDF-RT 8  and CISP2. 9 The choice of OWL-SKOS representations of CISP2 and ATC in our study comes from the fact that the PRIMEGE database adopts these nomenclatures, while the OWL representation of NDF-RT provides additional knowledge on interactions between drugs, diseases, mental and physical states. We extracted from the ATC OWL-SKOS representation the labels of the superclasses of the drugs listed in the PRIMEGE database, using the properties rdfs:subClassOf and member of on different depth levels thanks to SPARQL 1. 1 queries with property paths 10  (e. g. 'meprednisone' (ATC code: H02AB15) has as superclass 'Glucocorticoids, Systemic' (ATC code: H02AB) which itself has as superclass 'CORTICOSTEROIDS FOR SYSTEMIC USE, PLAIN' (ATC code: H02)).
Just like in the structure of the PRIMEGE database, some textual data must be distinguishable from each other when switching to the vector representation of EMRs, e. g. a patient's personal history and family history. To do this, we have introduced provenance prefixes during the creation of the bag-of-words to trace the contribution of the different fields. Concepts from knowledge graphs are considered as a token in a textual message. When a concept is identified in a patient's medical record, it is added to a concept vector. This attribute will have as value the number of occurrences of this concept within the patient's health record (e. g. , the concepts 'Organ Failure' and 'Medical emergencies' are identified for 'pancréatite aiguë', acute pancreatitis, and the value for these attributes in our concept vector will be equal to 1).
Dataset and Protocol We tested and evaluated our approach for enriching the vector representation of EMRs with ontological knowledge on a balanced dataset DS B containing data on 714 patients hospitalized and 732 patients not hospitalized. When the observation field is filled in by general practitioners, it can go from 50 characters to 300 characters on average. The best filled in fields concern prescribed drugs and reasons for consultations, then come the antecedents and active problems. Since we use non-sequential machine learning algorithms to assess the enrichment of ontological knowledge, we had to aggregate all patients' consultations in order to overcome the temporal dimension inherent in symptomatic episodes occurring during a patient's lifetime. Thus, all consultations occurring before hospitalization are aggregated into a vector representation of the patient's medical file.
For patients who have not been hospitalized, all their consultations are aggregated. Thus, the text fields previously described (Table  1 ) are transformed into vectors. We evaluated the vector representations by nested cross-validation  [3] , with an external loop with a K fixed at 10 and for the internal loop a K fixed at 3 with exploration of hyperparameters by random search  [1]  over 150 iterations. The different experiments were conducted on an HP EliteBook 840 G2, 2. 6 hHz, 16 GB RAM with a virtual environment under Python 3. 6. 3 as well as a Precision Tower 5810, 3. 7GHz, 64GB RAM with a virtual environment under Python 3. 5. 4. The creation of vector representations was done on the HP Elite-Book and on this same machine were deployed DBpedia Spotlight as well as domain-specific ontologies with Corese Semantic Web Factory  [6] ,  11  a software platform for the Semantic Web.
The regularization coefficient C and the norm used in the penalization were determined by nested crossvalidation. We opted for a bag-of-words model and the above cited machine learning algorithms since it is possible to provide a native interpretation of the decision of these algorithms, thus allowing the physician to specify the reasons for hospitalizing a patient with the factors on which he can operate to prevent this event from occurring. Moreover, logistic regression and random forest algorithms are widely used in order to predict risk factors in EHR  [9]. Finally, the limited size of our dataset excluded neural networks approaches.
Results In order to assess the value of ontological knowledge, we evaluated the performance of the machine learning algorithms by using the F tp,f p metric  [8]. Let T N be the number of negative instances correctly classified (True Negative), F P the number of negative instances incorrectly classified (False Positive), F N the number of positive instances incorrectly classified (False Negative) and T P the number of positive instances correctly classified (True Positive). T P f = K i=1 T P (i) F P f = K i=1 F P (i) F N f = K i=1 F N (i) F tp,f p = 2. T P f 2. T P f + F P f + F N f Table  4  summarizes the results for each representation and method combination tested on the DS B dataset: baseline: represents our basis of comparison where no ontological enrichment is made on EMR data i. e. only text data in the form of bag-of-words.
Discussion In general terms, knowledge graphs improve the detection of true positives cases, hospitalized patients correctly identified as such (Table  5  and 6 ) and provide a broader knowledge of the data in patient files like the type of health problem with CISP-2 (Table  7 ). Although the approach in combination with +s * does not achieve the best final results, this approach achieves the best overall performance among all the approaches tested with 0. 858 under logistic regression when using 8 K folds from the KFold during the training phase (Figure  3 ). It also surpasses other methods under 3 K partitions by exceeding the baseline by 0. 9% and at 4 K partitions by 0. 7% +t + s + c 2 + wa + wi which suggests an improvement in classification results if we enrich a small dataset with attributes from the enrichment provided by knowledge graphs.
Conclusion and Future Work In this paper, we have presented a method for combining knowledge from knowledge graphs, whether specialized or generalist, and textual information to predict the hospitalization of patients. To do this, we generated different vector representations coupling concept vectors and bag-of-words and then evaluated their performance for prediction with different machine learning algorithms. In the short term, we plan to identify additional concepts involved in predicting hospitalization of patients and to evaluate the impact of additional domain specific knowledge, as we focused mainly on drugs in our study. We also intend to propose an approach to automatically extract candidate medical concepts from DBpedia. Finally we plan to improve the coupling of semantic relationships and textual data in our vector representation, and support the detection of negation and complex expressions in texts.
F tp,f p for the different vector sets considered on the balanced dataset DSB. + c2 + wa + wi 0. 8258 0. 8486 0. 8547 0. 8430 +t + s * +c2 + wa + wi 0. 8239 0. 8494 0. 8543 0. 8425 +t + c2 + wa + wi 0. 8140 0. 8531 0. 8571 0. 8414.
Target's physical location can be further inferred by using geometric algorithms (e. g. , trilateration or triangulation). Nevertheless, the performance of geometric mapping heavily relies on the Line-of-Sight (LoS) condition. The rich indoor multipath and shadowing effects, to a great extent, blur the monotonous relation between physical measurements and distances, complicate RF propagation modeling, and thus degenerate positioning accuracy. As an alternative to analyze the sophisticated signal propagation, location fingerprinting adopts a pattern-matching approach. The main idea is to collect signal features from predefined RP locations in the area of interest to construct a fingerprint radio map (a. k. a. site survey) in the offline phase. Subsequently, in the online phase, localization can be simply accomplished by matching the measured fingerprint at an unknown location with those in the offline database to return the best-fitted location estimation.
A. OVERVIEW The overall architecture of the proposed system is illustrated in Fig. 1. In general, it consists of two major functionality components: the offline fingerprint radio map construction and the online target's position estimation. For CSI fingerprint database construction, once getting the received raw CFR packets through signal war-driving, we first employ a tap filtering based pre-processing scheme to extract the most informative and location-dependent components in the multipath-rich indoor scenario. Subsequently, we model the statistical features of filtered CFR amplitude by calculating an AR modeling based entropy metric and then build a representative fingerprint radio map after removing the ambiguous endpoint subcarriers. Afterwards, for online location estimation process, when a mobile target arrives into the area of interest, it executes the same procedures to acquire the entropy vector and matches against the learned offline attributes.
Besides, most probabilistic approaches require sufficient number of measurements stored in the fingerprint database, which guarantees an accurate PDF estimation but suffers huge system burden. Therefore, in this paper, we resort to the well-known Shannon entropy  [41]  as the fingerprint alternative in our localization system. Given the offline and online CFR amplitude PDF estimates pH s m (β; r) and pG s θ (β; r), both of which are from the r th subcarrier and the s th AP. For the simplicity of presentation, here we define β as a general expression of CFR amplitude from the same subcarrier. Thus, the offline entropy definition can be expressed by φr H s m = - ∞ -∞ pH s m (β; r) log pH s m (β; r)dβ (5) Similarly, the online CFR entropy φr G s θ can also be calculated as the fingerprint for the subsequent stage of target's location determination.
C. ONLINE POSITION ESTIMATION For the online location determination, the mobile target is required to be accurately mapped to the pre-designed radio map. To quantitatively measure the similarity between the stored entropy fingerprints and the estimated online CFR entropies, we employ Manhattan distance  [22]  which is also known as taxicab metric, capable of measuring the gap between two points through the summation of the absolute differences of their corresponding components. Given the offline and online entropy fingerprints ˆ H s m and ˆ G s θ , we define the Manhattan distance between them as D s m = ˆ H s m -ˆ G s θ 1 = R i=1 φi H s m -φi G s θ ( 16 ) where • 1 denotes the 1 norm. It concisely reveals the physical similarity between the online fingerprints at an unknown position and the offline dataset at the m th RP location, both of which are measured from the s th AP.
A. EXPERIMENTAL SETUP 1) EXPERIMENTAL PRESENTATION (a) Environment: The entire experiments are conducted in the CEDRIC laboratory of CNAM (a typical office environment in a multistorey building as shown in Fig. 7a ). This lab office is a large room with an area of over 200 m 2. The indoor space is partitioned into several office and meeting rooms with many desks, chairs, computers, shelves furnished inside, which forms a complex radio propagation environment. The whole CSI database was collected during the working time in February, 2019. (b) Configuration: We conduct our real experiments on commodity-ready off-the-shelf Wi-Fi devices  [17]. Specifically, by working in the 5GHz band of IEEE 802. 11n monitor mode, we use an HP Elitebook 8530w laptop as the signal transmitter (TX) and an Humming-Board Pro (HMB) as the mobile receiver (RX), which are exhibited in Fig.
7b   run 64-bit Ubuntu 14. 04 OS and Debian 8. 0 OS, respectively. Additionally, for our antenna settings, each Wi-Fi device is equipped with three omni-directional antennas to support 3 × 3 MIMO configuration. (c) Implementation: As mentioned above, we implement the CSI data collection in our lab scenario. Fig. 8  shows the floor plan of this 15 m×15 m laboratory with a main corridor alongside several office and meeting rooms. The HP laptop serving as signal transmitter is fixed on the table of the central office room. Under injection mode, it is designed to intermittently transmit at the rate of 100 packets per second using only one transmitting antenna. It is worth mentioning that one transmitter setting is highly sufficient and well-performed in this lab scenario. If necessary, we may resort to multiple transmitters for the future larger testbed.
Acknowledgments We thank the reviewers of the  IACR Transactions on Symmetric Cryptology (ToSC)  for their detailed comments, which helped to improve the quality of the paper. We also thank  Martijn Stam  for his participation in the early stages of this work. Farshim was supported by the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013 Grant Agreement no. 339563 -CryptoCloud ). Fuchsbauer was supported by the  MSR-Inria Joint Centre  and the  French ANR  project  EfTrEC  ( ANR-16-CE39-0002 ).
To keep the extent of the numerical results within reason, we implement this alternative version of ABC only in the case of the four auxiliary GARCH models. The AUKF evaluation of the likelihood function (and joint score) associated with (  22 ) and (  23 ) is performed using the GAUSS software, with the integration required to produce the marginal score function implemented using a numerical integration subroutine from C. All other computations are implemented in MATLAB and R. 10 Each instance of ABC is based on N = 50, 000 simulated draws from uniform priors on φ 1 , φ 2 and φ 3 truncated as described above. Draws are retained that lead to distances within the 0. 5% quantile of the overall simulated distances, which are intrinsic to each procedure. Finally, two filtering-based methods are used to produce an estimate of the exact posterior via PMMH.
SUPPLEMENTARY MATERIAL: The Supplementary Appendix contains: 1) the proofs of Theorems 1 and 2; 2) an informal demonstration of the equivalence of the auxiliary MLE-based approach to ABC and the corresponding approach based on the auxiliary score; 3) additional numerical exercises demonstrating the large sample behavior of ABC; and 4) implementation details for the AUKF in the SV-SQ example. Gauss, C, Matlab and R code used to produce all numerical results in the paper have also been provided on-line.
*    The first and third authors were partially financed by  Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia)  within the Project  UID/MAT/00013/2013. † The three authors were partially financed by  COST action CA15123 EUTYPES. ‡ An  early phase of the reported work was partially financed by the project  Climt ,  ANR-11-BS02-016 , of the  French Agence Nationale de la Recherche.
The 1,546 galaxies or groups included in this subsample have a median redshift of z = 0. 009. (iii) TF data is particularly deep within the region covered by Arecibo Telescope which observes galaxies of declination δ ∈ [0, 38] •. The subsample contains 1,628 galaxies or groups with a median redshift of z = 0. 023. (iv) Other TF data than in the Arecibo declination range. The subsample contains 1,569 galaxies or groups with a median redshift of z = 0. 019. (v) Other data coming from heterogeneous methodologies. Cepheids, tip of the red giant branch and surface brightness fluctuation contributions are local and of high accuracy. Groups which have more than ten members with known redshifts are included in this subsample. Group distances are mostly averaged over Fundamental Plane and TF measures with occasional SNIa contributions.
THE COSMICFLOWS -3 PECULIAR VELOCITY FIELD In this section we present the results of the method applied on CF3 catalog. We chose to assume (Ω m , Ω Λ , H 0 ) = (0. 3, 0. 7, 75. 0). We fit the distance priors by the empirical function defined by Eq. 18 for the subsamples (ii,iii,iv) defined in Section 2. 3. The distances from the subsample (i) corresponding to 6dF data are fitted assuming the function described by Eq. 17. We do so to ensure the possibility for the prior function to model a sharp cut-off in distances. We fit for a global effective zero-point shift h eff and assume the inter-calibration of distances determined by  Tully et al. (2016)  and the CMB frame transformations given by  Tully et al. (2008). Figure  2  shows the resulting MCMC chain for the two parameters σ NL , h eff and for the overdensity field reconstructed near the Virgo Cluster at location (SGX, SGY, SGZ) = (-3.
COSMOGRAPHY OVERVIEW In this section we show partial results on the reconstructed overdensity field. A full description will be made in an upcoming cosmography paper. Main results are presented in Figure  8  where we plot the overdensity field in three different slices corresponding to coordinates SGZ = 0 Mpc/h 75 (twice), SGX = 0 Mpc/h 75 and SGY = -93 Mpc/h 75 going from top left to bottom right. We first look at the two top panels. On the left, the colored dots represent CF3 data within a slab of 10 Mpc/h 75 thickness, while on the right the dark dots are all available galaxies of the LEDA database located at their redshift positions within the same depth. The colored galaxies show the anisotropy of CF3 catalog. The southern hemisphere is mainly covered by 6dF galaxies (in red), while there are fewer data points in the northern hemisphere.
With only about 11,000 tracers, we were able to map and identify overdense and underdense regions in the Local Universe, and showed the good agreement with redshift surveys containing more than 150,000 galaxies. As suggested by  Lavaux (2016) , the model could be extended to estimate cosmological parameters such as the Hubble constant H 0 or the growth rate f σ 8. With upcoming large distance datasets, coming from  TAIPAN (da Cunha et al. (2017) , ∼ 50, 000 FP distances), WALLABY  (Duffy et al. (2012) , ∼ 60, 000 TF distances) and LSST  (Lochner et al. (2018) , ∼ 100, 000 SNIa distances), peculiar velocity analyses will need an accurate model to avoid systematics in the determination of cosmological parameters. The method presented here is to be considered as a baseline of such model. first 300 are considered as the warmup phase.
ACKNOWLEDGEMENTS R. G. thanks  Mickael Rigault  for fruitful discussions. We are grateful to the colleagues maintaining LEDA Lyon extragalactic database  Dmitry Makarov  and  Philippe Prugniel. This research was supported by  Institut Universitaire de France  and  CNES. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  759194 -USNAC ).
Modern verification projects continue to offer new challenges for formal verification. One of them is the linked list module of Contiki, a popular open-source operating system for the Internet of Things. It has a rich API and uses a particular list representation that make it different from the classical linked list implementations. Being widely used in the OS, the list module is critical for reliability and security. A recent work verified the list module using ghost arrays. This article reports on a new verification effort for this module. Realized in the Frama-C/Wp tool, the new approach relies on logic lists. A logic list provides a convenient high-level view of the linked list. The specifications of all functions are now proved faster and almost all automatically, only a small number of auxiliary lemmas and a couple of assertions being proved interactively in Coq.
INTRODUCTION Nowadays, the Internet of Things (IoT) software has become a new area of application for formal verification. Billions of connected devices and services are in use, and their number is continuously growing. This many devices connected to the internet raises many security risks, which can be addressed using formal methods. Context and Motivation. Formal verification of IoT software brings new verification targets and challenges. In this paper, we consider the linked list module of Contiki, a widely used open-source operating system for IoT devices. Contiki  [10]  provides basic features needed from any OS as well as some others more specific to IoT devices, such as sensor drivers and full low-power IPv6 connectivity, including 6TiSCH, 6LoWPAN, RPL, or CoAP standards. Contiki is written in C with a focus on memory optimization and power consumption, as targeted devices are generally small battery-operated chips.