dataesr/scientific-paragraphs-categorization huggingface dataset:
As presented in previous research  [18, 21, 22, 56] , the IoT concept can best proliferate if more standards and open solutions are adopted. This is a position with which we strongly agree, therefore our choice of using open hardware, open software and major standards. On the hardware costs of the CRIP, they were relatively high, but mainly due to the cost of the biometric module, which represented about 50% of the total cost. The remaining hardware could be easily acquired and assembled. Other options were considered for the different hardware peripherals, but the constraints to USB devices to ease the assembly while making it flexible, limited our options to the ones presented. It is interesting to note that the last Raspberry Pi 3 already includes Bluetooth and Wi-Fi, which can make the solution even cheaper and simpler to develop.
We noticed that the same manufacturer already offers new devices that, they state, can be paired with multiple devices, but it is an open question that still needs further evaluation. As a third limitation we would like to point out some issues regarding the security and privacy of the CRIP, as they can be helpful for designers of similar platforms, although we will not delve much in these topics. On these prototypes, three security mechanisms were used, namely fingerprints for users' authentication and identification, NFC for users and devices identification, and HTTPS for communications between the CRIP and the CAALHP. Although these mechanisms provide a good level of security on the system, they are not enough, especially from the privacy point of view. On the users' identification/authentication, the fingerprint can be considered one of the best that exists and the selected module provides a good level of security since the fingerprints can be stored on hardware, which makes its cracking more difficult, but fingerprint distribution still raises many questions as they can pose major security failures due to unauthorized access and compromise users security and privacy.
This is a topic that still needs further research. On the use of NFC, we can only guarantee identification, not authentication  [57]. This is an inherent problem of the technology that, in our opinion, can only be managed by setting a proper PKI. During the development, we did not address data exchanges security on read and write NFC tags, despite their availability. This is something that we intend to address in future developments of the CRIP. Finally, a word on the users data privacy. At this point, the CRIP had implemented a minimal privacy mechanism. The only data stored on a local database are the user IDs from the NFC cards, associated with a random fingerprint ID that was generated when a user enroll on the system. It is important to note that the fingerprint templates were always stored on hardware and never on any other support.
Acknowledgments: This research work was supported under the  European Framework Program FP7 Research for the Benefit of SMEs , project  FP7-SME-2012-315158-CareStore. The authors would also like to acknowledge the work of all the members of the CareStore team, without whom this work would not be possible. main industry's project manager of the  CareStore  project. All authors contributed on the article writing with critical reviews and corrections.
A new algorithm for the determination of the initial flavour of B 0 s mesons is presented. The algorithm is based on two neural networks and exploits the b hadron production mechanism at a hadron collider. The first network is trained to select charged kaons produced in association with the B 0 s meson. The second network combines the kaon charges to assign the B 0 s flavour and estimates the probability of a wrong assignment. The algorithm is calibrated using data corresponding to an integrated luminosity of 3 fb -1 collected by the LHCb experiment in proton-proton collisions at 7 and 8 TeV centre-of-mass energies. The calibration is performed in two ways: by resolving the B 0 s -B 0 s flavour oscillations in B 0 s → D - s π + decays, and by analysing flavour-specific B * s2 (5840) 0 → B + K -decays.
Introduction Precision measurements of flavour oscillations of B 0 (s) mesons and of CP asymmetries in their decays allow the validity of the standard model of particle physics to be probed at energy scales not directly accessible by current colliders  [1]. Measurements of associated observables, e. g. the CP -violating phase φ s in B 0 s → J/ψ K + K -and B 0 s → J/ψ π + π -decays  [2, 3] , are among the major goals of the LHCb experiment and its upgrade  [4, 5]. 1 These analyses require so-called flavour-tagging algorithms to identify the flavour at production of the reconstructed B meson. Improving the effectiveness of those algorithms is of crucial importance, as it increases the statistical power of the dataset collected by an experiment. Several types of flavour-tagging algorithms have been developed in experiments at hadron colliders.
The previous SSK algorithm used by the LHCb experiment has a tagging power of 0. 9% and 1. 2% in B 0 s → J/ψ φ and B 0 s → D - s π + decays, respectively. For comparison, the performance of the combination of the OS algorithms in these decays corresponds to a tagging power of about 2. 3% and 2. 6%  [11, 12]. The calibration function ω(η) is obtained with control samples of flavour-specific decays, i. e. decays in which the B flavour at decay is known from the charge of the final-state particles. In the case of the new SSK algorithm described here, the decay B 0 s → D - s π + and, for the first time, the decay B * s2 (5840) 0 → B + K -are used. These decays are reconstructed in a dataset corresponding to an integrated luminosity of 3 fb -1 collected by LHCb in pp collisions at 7 and 8 TeV centre-of-mass energies.
Conclusion A new algorithm for the determination of the flavour of B 0 s mesons at production has been presented. The algorithm is based on two neural networks, the first trained to select charged kaons produced in association with the B 0 s meson, and the second to combine the kaon charges to assign the B 0 s flavour, and to estimate the probability of an incorrect flavour assignment. The algorithm is calibrated with data corresponding to an integrated luminosity of 3 fb -1 collected by the LHCb experiment in proton-proton collisions at 7 and 8 TeV centre-of-mass energies. The calibration is performed in two ways: by resolving the B 0 s -B 0 s flavour oscillations in B 0 s → D - s π + decays, and, for the first time, by analysing flavour-specific B * s2 (5840) 0 → B + K -strong decays. The tagging power of the new algorithm as measured in B 0 s → D - s π + decays is (1.
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands);  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MinES  and  FANO (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF  (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
Adding the plane axial position allows for the absolute real z-position to be determined. Third, the obtained positions are combined to a single localization file: detections appearing on consecutive planes are combined and averaged (Fig. 2(b) ). When plane spacing is less than the depth of focus, molecules are expected to appear on two consecutive planes. If the localization appears on a single plane, then it is discarded as it is likely to be a false positive. This notably increases the quality and fidelity of the localizations. As an exception rule, localizations around the first and last planes are preserved without correlating them with adjacent planes. All computational and image analysis tools are custom-written in MATLAB. To verify the robustness of this technique, we imaged beads immobilized on a coverglass using the astigmatic MFM and displaced the beads with discrete axial steps separated by 300 nm.
Acknowledgments B. H. acknowledges the financial support of the  Pierre Gilles de Gennes Foundation , and  Agence pour la Recherche sur le Cancer (ARC Foundation ). We acknowledge financial support from  French National Research Agency (ANR) Paris-Science-Lettres Program  ( ANR-10-IDEX-0001-02 PSL ),  France-Bio-Imaging infrastructure supported by ANR  Grant  ANR-10-INSB-04  (Investments for the Future) and the  Institut Curie International PhD Program.
The data from 2014 were used to analyse the effect of the landscape on the abundance of small mammals, but these data were not used in the analysis of the small mammal-tick relationships. As the sampling effort was the same for each site and season, we used the total number of captured animals per trap-line as an index of the true small mammal abundance  [46]. In 2012, ectoparasites were removed from small mammals and conserved in 70 % ethanol, either immediately or after deep-frozen storage (-20 °C) of the carcasses. Ticks were identified to stage (larva, nymph or adult) and species using a binocular microscope and the identification key of Pérez-Eid  [47]. We identified two-thirds of the larvae and they all belonged to the same species, I. ricinus. We therefore assumed that all larvae belonged to this species and that all other tick species were rare.
Statistical analyses Checking for spatial autocorrelation and species interactions To assure the independence of the samples, we controlled for spatial autocorrelation. We computed Moran's I for small mammal species abundance, questing nymph abundance and larval occurrence in each season  [53]. The exclusion of one important host species by another could lead to an indirect effect of the latter on the nymph abundance. To detect potential competition between wood mice and bank voles (the two most frequently trapped species), we tested the relationship between their abundances with linear models on all the data, the data separated by year (2012, 2013 and 2014) or by season (spring and autumn).
The effect of landscape features on small mammal abundances and questing immature I. ricinus ticks To test the effect of the landscape features and the sampling year, we modelled each of three response variables: (1) small mammal abundance, (2) I. ricinus larval occurrence index, and (3) questing nymph abundance as a function of the extracted landscape variables and the sampling year. The small mammal abundances are count data and the larval occurrence index follows the definition of a Poisson error distribution, we therefore used generalized linear models with a Poisson error distribution (GLMPs) and multiple explanatory variables. We used a negative binomial error distribution (GLMNBs) The landscape variables were extracted in a buffer of 250 metres around the trap-lines. For each landscape variable, we give a definition, the units, and we indicate the expected effects on the abundances of small mammals and I.
ricinus ticks a Effect considered independently of small mammal abundances b In the calculation of the ENN-Wood the values computed for large wooded patches have a greater weight, proportional to their area to account for the over-dispersion of the nymph abundance data  [54]. To facilitate the comparison of slopes between variables measured in different units, all landscape variables were scaled to z-scores (mean of zero and a standard deviation of 1). For model simplification, the best model was selected according to AICc (Akaike Information Criterion corrected). To determine the significant variables in the simplified models, we chose an alpha level of 0. 05. The p-values of the simplified models were calculated by applying a type III ANOVA to the model. We conducted separate analyses of nymph abundance for the spring and autumn, despite the fact that few nymphs were collected in autumn.
Acknowledgement This work was funded by the  French National Research Agency  ( ANR-11-Agro-001-04 ; call for Proposal 'Agrobiosphere',  OSCAR  project). Grégoire Perez  (PhD) was supported by a fellowship from the  Brittany region, France. We thank all the colleagues and trainees who helped with the field or lab work ( Françoise Armand ,  Jean-Yves Audiart ,  Manon Balbi ,  Didier Billon ,  Guillaume Bouger ,  Floriane Boullot ,  Mohammed-Lemine Brahim ,  Nathalie De la Cotte ,  Romain Georges ,  Mathieu Gonnet ,  Diab al Hassan ,  Caroline Hervet ,  Thierry Hoch ,  Olivier Jambon ,  Maggy Jouglin ,  Hélène Mahé ,  Laurence Malandrin ,  Emmanuelle Moreau ,  Pranav Pandit ,  Ionuţ Pavel ,  Elsa Quillery ,  Jessie Renaud & Rémy Vermesse ). We thank the " Zone atelier Armorique " (http://osur.
␤-Mercaptoethanol was added to various amounts of supernatant and analyzed by SDS-PAGE (7. 5% gels; Bio-Rad Laboratories) and Western blotting. A rabbit anti-AMH polyclonal Ab was used to detect AMH and an antirabbit Ab conjugated to streptavidin-horseradish peroxidase (HRP) (Jackson ImmunoResearch) was used as the secondary Ab. The bands were quantified using the Image J software (National Institutes of Health, Bethesda, Maryland).
Acknowledgments We thank the  IVF laboratory of Antoine Béclère Hospital  for technical assistance. Address all correspondence and requests for reprints to:  Nathalie di Clemente ,  Université Paris 7, INSERM ,  Centre National de la Recherche Scientifique , Paris cedex 13, France. E-mail: nathalie. diclemente-besse@univ-paris-diderot. fr. This work was supported by  Idex Sorbonne Paris Cité and Agence Nationale pour la Recherche  (Grant  ANR-12-BSV1-0034-01 ) to N. d. C. Disclosure Summary: A patent application ( EP14153715. 9 )has been filed on the AMHRII-Fc/Fc soluble receptor by  R. L. C. and  N. d. C. The other authors have nothing to disclose.
Introduction Matrix-Matrix Multiplication (MMM) is an important kernel in most varied domains and application areas. Its performance is of great practical importance and it highly depends on memory management; the most performance critical Address(es) of author(s) should be given sub-problems are these of finding the schedules with the minimum numbers of i) L1 data cache accesses, ii) L2 data cache accesses, iii) L3 data cache accesses, iv) main memory data accesses, v) addressing instructions. The above sub-problems depend on each other (e. g. , a decrease on the number of L2 data cache accesses will consequently increase the number of L1 data cache accesses) and this is why they should be addresses together as one problem and not separately (in  [1] , a methodology for Matrix-Vector Multiplication is given).
The state of the art (SOA) hand/self-tuning libraries for linear algebra, such as ATLAS  [16] , OpenBLAS  [17] , Eigen  [18] , Intel MKL  [19] , PHiPAC  [20]  and a few of the many noteworthy papers from the past such as  [21]   [22]  [23]  [24]  for CPUs and  [25]   [26]  for GPUs, do not give a theoretical solution; instead, they find the performance critical parameter values mostly by searching and by using heuristics and empirical techniques. The selection of the parameter values is a difficult and time consuming task for two reasons. First, many parameters have to be taken into account, such as the number of the levels of tiling, tile sizes, loop unroll depth, software pipelining strategies, register allocation, code generation, data reuse, loop transformations. Second, the optimum parameters for two slightly different architectures are different.
g. , microcontrollers, smaller processors with one level of data cache, processors with direct mapped data cache. Third, this methodology takes into account more hardware architecture parameters (the memories' latencies, the data cache line size, the number, the latencies and the type of the CPU function units, the number of the load/store units) and software characteristics (number of matrix operations, number of addressing instructions). Fourth, the proposed methodology, due to the major contribution of number three above gives a smaller search space, a smaller code size and a smaller compilation time, as it does not test a large number of alternative schedules. The proposed methodology is compared with the state of the art software libraries of ATLAS and Intel MKL. Although a performance comparison with Intel MKL is unfair, a detailed experimental analysis has been made as it is the fastest MMM library in the world for Intel general purpose processors.
A performance comparison with Intel MKL is unfair for two reasons. First, Intel MKL developers have access to all the Intel processor architecture details which we do not, e. g. , victim cache, hardware prefetchers; this is why Intel MKL library is the fastest library on Intel processors only. Second, Intel MKL loop kernels are written in assembly code while our method in C (assembly code is always more efficient); Intel developers write assembly code to deal with the low level transformations, e. g. , register allocation, instruction selection and instruction scheduling. The proposed methodology lies at a higher level of abstraction and it is used to wide range of computer architectures. Implementing the proposed methodology in assembly code is beyond the scope of this paper and thus the low level transformations are applied by the target compiler (which is less efficient).
Related Work The problem of speeding up MMM is studied the last decates. A historical perspective is given in  [31]   [32]   [33]  [34]  [35]   [36]  [37]  [38]   [39]  [40]  [41] ; these works present how hardware and software can work on scalable multi-processor systems for matrix algorithms. ATLAS  [16]  [42]  [43]  [44]  [45]  is an implementation of a high performance software production/maintenance called Automated Empirical Optimization of Software (AEOS). In an AEOS enabled library, many different ways of performing a given kernel operation are supplied, and timers are used to empirically determine which implementation is best for a given architectural platform. ATLAS uses BLAS implementations. The BLAS (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations.
Although SRUMMA minimizes the communication contention between CPUs, it does not optimize the MMM problem for one CPU (it runs the cblas sgemm AT-LAS optimized routine). Furthermore, about half of the above works, use the Strassen's algorithm  [65]  to partition the MMM problem into many multi-core processors; Strassen's algorithm minimizes the number of the multiplication instructions sacrificing the number of add instructions and data locality. The MMM code for one core, is either given by Cilk tool  [66]  or by cblas sgemm routine of ATLAS. At last,  [67]  and  [68]  show how shared caches can be utilized. All the above works, are empirical techniques and do not provide a theoretical model. Regarding GPUs, several related works exist such as  [25]   [76]. Reference  [26]  show how to modify the MAGMA GEMM kernels in order to use more efficient the Fermi architecture.
[69]  presents a method for producing MMM kernels tuned only for a specific architecture, through a canonical process of heuristic autotuning, based on generation of multiple code variants and selecting the fastest ones through benchmarking. [71]  provide implementations of Strassen's MMM algorithm as well as of Winograd's variant; they show that only for square matrices of very large sizes (16384 × 16384) achieve 33% speedup over cblas sgemm (ATLAS routine for data of type float) and 21% over cblas dgemm (ATLAS routine for data of type double). [73]  presents an in-depth study to reveal interesting trade-offs between shared memory and the hardware-managed L1 data caches for MMM. [74]  investigates different performance techniques such as tiling, memory coalescing, prefetching, and loop unrolling, in trying to evaluate which method is the most efficient.
Given that the proposed methodology achieves about the same number of load/store and arithmetical instructions and a lower number of DDR accesses (for medium and large input sizes it the most performance critical parameter) than MKL at all cases, we strongly believe that the small performance loss is due to the four following low level optimizations: a) MKL uses a more efficient AVX instruction set (especially when unpacking and storing the multiplication results), b) MKL achieves a lower Taddressing value due to the more efficient hand-written assembly code, c) the sequence of MKL assembly instructions is more efficient and thus the number of idle cycles in the pipeline is minimized, d) MKL uses in a more efficient way some Intel processor units (e. g. , victim cache, hardware prefetchers) since it has access to the hardware details, e) software prefetch instructions are written at the exact code line.
The proposed methodology is also compared with two different embedded processors, i. e. , PowerPC-440 on Xilinx Virtex-5 FPGA and ARMv7-a on GEM5 simulator (Fig. 16 ). Given that there is no state of the art software library for these processors, we evaluated our methodology with 'arm-linuxgnueabihf-gcc' and Xilinx compiler, on ARM and PowerPC, respectively. As  Furthermore, an evaluation of Subsect. 3. 1. 1 (small input sizes -ineq. 5 holds) is made on SimpleScalar simulator (Fig. 17 ). In Fig. 17 , the number of load/store instructions is shown, for different tile sizes; square matrices of size N = 216 are taken. Since ineq. 5 holds, all the data of B and 2 × i 0 rows of A, fit in L1 data cache (Fig. 2 ). In SimpleScalar simulator, 10 available floating point registers exist and according to ineq.
Finally, an evaluation over different cache size and associativity values is made on GEM5 simulator using ARMv7-a processor for five different cache sets (L1 size, L1 associativity) (Fig. 19  and Fig. 20 ). The compiler used is 'arm-linux-gnueabihf-gcc' and the input size is N = 80 (floating point values and square matrix sizes). Regarindg gcc compiler, by changing the cache parameters, its performance does not change significantly, except from the case that L1=4 kbytes, where its performance is about 5 times lower; this is because the row of A cannot remain in L1 cache in this case and both A and B elements are always loaded from DDR. In order to the row of A remains in L1, one row of A and one column of B have to be smaller than L1 size (otherwise, the A elements are spilled); however, B is written row-wise in main memory (by default) and all the column elements are written in no consecutive main memory locations; thus for one column element to be fetched, an entire L1 cache line is fetched (cache line size is 64 bytes).
Whole genome sequencing, assembly and cgMLST for phylogenomic analysis Genomic DNA was extracted and whole-genome sequencing was then performed using Illumina HiSeq 2000 sequencer or by Illumina MiSeq sequencer. After sequencing, the reads were quality-trimmed and then assembled using the CLC Genomics Workbench software version 6. 0 (CLC bio, Aarhus, Denmark). The resulting assembly files were exported as ACE files and imported into SeqSphere + software version 2. 3 (Ridom GmbH, Münster, Germany). The assembly contigs together with epidemiologic meta-data were also deposited to the Neisseria BIGSdb website  [22]. A core genome multi-locus sequence typing (cgMLST) target set was determined using all finished N. meningitidis genomes available in GenBank as of February 2014 (n = 14)  [23]. The genome of strain FAM18 (NC_008767) was used as a reference.