dataesr/scientific-paragraphs-categorization huggingface dataset:
In this paper, we propose a symbolic control synthesis method for nonlinear sampled switched systems whose vector fields are one-sided Lipschitz. The main idea is to use an approximate model obtained from the forward Euler method to build a guaranteed control. The benefit of this method is that the error introduced by symbolic modeling is bounded by choosing suitable time and space discretizations. The method is implemented in the interpreted language Octave. Several examples of the literature are performed and the results are compared with results obtained with a previous method based on the Runge-Kutta integration method.
Numerical experiments and results This method has been implemented in the interpreted language Octave, and the experiments performed on a 2. 80 GHz Intel Core i7-4810MQ CPU with 8 GB of memory. The computation of constants L j , C j , λ j ( j ∈ U) are realized with a constrained optimization algorithm. They are performed using the "sqp" function of Octave, applied on the following optimization problems: • Constant L j : L j = max x,y∈S, x =y f j (y) -f j (x) y -x • Constant C j : C j = max x∈S L j f j (x) • Constant λ j : λ j = max x,y∈T, x =y f j (y) -f j (x), y -x y -x 2 R S δ B( x 3 1 , δ π3 1 ) x3 Likewise, the convexity test B( x 3 2 , δ π3 2 ) Z 3 Z 1 Z 4 Z 2 R S Z 2 '= Post i1 ( Z 2 ) Post i2 ( Z 2 ') Tube i1 ( Z 2 ) Tube i2 ( Z 2 ') d 2 (δ j (t)) dt 2 > 0 can be performed similarly.
The evolution of both general purpose and domain-specific meta-models and its impact on the existing models and modeling tools has been discussed extensively in the modeling research community. To assess the impact of domain-specific meta-model evolution on the modeling tools, a number of measures have been proposed by Durisic et al. , NoC (Number of Changes)  being the most prominent one. The proposed measures are evaluated on a case of AUTOSAR meta-model that specifies the language for designing automotive system architectures. In this paper, we assess the applicability of these measure and the underlying data-model for their calculation in a case study of Modelica and UML meta-models. Our preliminary results show that the proposed data-model and the measures can be applied to both analyzed meta-models as we were able to capture 68/77 changes on average per Modelica/UML release.
We achieve this goal in a case study of two additional meta-models: Modelica  (Modelica, 2014)  and UML meta-model. Modelica meta-model is used to define the language for modeling complex systems containing mechanical, electrical, electronic, and other components. UML is a well known general-purpose metamodel that defines the language that can be used for modeling a variety of different domains. Our research question is defined as: What is the level of applicability of the measures of domainspecific meta-model evolution and the underlying data-model defined in  (Durisic et al. , 2014)  for monitoring the evolution of Modelica/UML meta-models? The results of our study show that the analyzed data-model used for the definition of NoC and other measures described in Section 2 can be applied to the meta-models of Modelica and UML.
Modelica Data-model For calculating the measures between different releases of the Modelica meta-model, we used a subset of the AUTOSAR data-model presented in Figure  2. The relationship between one Package and its sub-Packages has been kept in case of future use, but it has not been used for the measurements because the Modelica meta-model does not contain any subpackages. Additionally, the Elements, Attributes and Connectors in Modelica do not contain any additional semantics captured in a form of TaggedValues, nor do they have Stereotypes and UUIDs (unique identifiers). Therefore, we did not use these elements from the AUTOSAR data-model for the measurement.
2 ). The head of the patient is introduced in the chamber as shown in Fig. 2. A special thin membrane is used for isolating the human head from the matching liquid and keeping the liquid within the chamber. A carbon loaded silicon rubber (CLSR) is also used for reducing reflection from boundary conditions (Fig. 3 ). A switching matrix connected to a network analyzer selects the transmitting and receiving antennas. The system is potentially delivering a 160 × 160 matrix of S parameters. The measured S parameters due to the scattered field of an object under investigation are obtained by complex subtraction between two measurements with empty chamber and with the head, respectively. The raw data can be wirelessly transferred to a remote computing center. The HPC machine will compute the tomographic images, which can be quickly transferred from the computing center to the hospital.
Notice that R s T is then the extension matrix from the subdomain Ω s to Ω. The matrix D s is a #N s × #N s diagonal matrix that gives a discrete partition of unity, i. e. M ORAS -1 = R s T D s A s -1 R s s=1 N sub ∑ which is called Optimized Additive Schwarz (OAS), would be symmetric for symmetric problems, but in practice it gives a slower convergence with respect to M ORAS -1  [10]. These domain decomposition preconditioners are implemented in the HPDDM library  [12] , an open source high-performance unified framework for domain decomposition methods. HPDDM can be interfaced with various programming languages and open source finite element libraries such as FreeFem++, which we use in the simulations.
IX. CONCLUSION The idea behind this work comes from the paradigm to develop a (portable/transportable) microwave imaging system whose raw data are wirelessly transferred to a HPC. The HPC machine will then compute the 3D image of the patient's brain. Once reconstructed, the image is quickly transmitted from the computing center to the hospital for stroke detection (including ischemic/hemorrhagic discrimination) and monitoring during treatment. We have developed a tool that reconstructs a tomographic microwave image of the brain in 94 seconds on 4096 computing cores. This computational time corresponds to clinician acceptance for rapid diagnosis or medical monitoring at the hospital. These images were obtained from corrupted synthetic data from a very accurate model of the complex permittivity of the brain.
Automated sample changers are now standard equipment for modern macromolecular crystallography synchrotron beamlines. Nevertheless, most are only compatible with a single type of sample holder and puck. Recent work aimed at reducing sample-handling efforts and crystal-alignment times at beamlines has resulted in a new generation of compact and precise sample holders for cryocrystallography: miniSPINE and NewPin [see the companion paper by Papp et al. (2017, Acta Cryst. , D73, 829-840)]. With full data collection now possible within seconds at most advanced beamlines, and future fourthgeneration synchrotron sources promising to extract data in a few tens of milliseconds, the time taken to mount and centre a sample is rate-limiting. In this context, a versatile and fast sample changer, FlexED8, has been developed that is compatible with the highly successful SPINE sample holder and with the miniSPINE and NewPin sample holders.
, 2004) , but most are only compatible with a single sample holder and puck model currently used in cryocrystallography. The FlexED8 sample changer shown at ESRF beamline BM14. MD2S diffractometer with SmartMagnetP, cryo-jet and MAR CCD X-ray detector, Flex robotics with a tool rack, Sta ¨ubli TX 60L six-axis robot equipped with a tool changer and a miniSPINE/NewPin gripper, Datamatrix reader, camera and ED8 EdgeDewar. France) powered by a CS8C controller. Ancillary functions are controlled through Modbus electronic modules (WAGO Contact SAS, Roissy, France) connected to the CS8C via an ethernet port (Supplementary Fig. S1 ). In addition to the robot arm, the Flex robot includes the following (Fig. 2 ): (i) a tool changer mounted on the robot arm and a tool-storage rack that can hold different grippers and an alignment probe; (ii) a machine-vision system used to calibrate the grippers and control the position of the sample holders in the gripper during their transfer; (iii) a Datamatrix reader to track the samples and (iv) a deicing station to remove frost accumulated on the grippers during sample manipulation.
Control of the FlexED8 sample changer The sample changer is controlled by high-level software written in Java, based on the JLib library (EMBLEM Technology Transfer GmbH, Heidelberg, Germany; http:// software. embl-em. de). The control software runs on a Windows PC, accessible by a graphical user interface (Supplementary Figs. S3  and S4 ), and a dynamically generated socket server on the local network using the Exporter protocol (part of the JLib library). The MXCuBE experiment-control software  (Gabadinho et al. , 2010)  connects to this server, providing user access to the basic functionalities and error messages (Supplementary Fig. S5 ). Alternative communication protocols such as Tango, Tine, Epics or Java Web Services are also available for integration in other beamline-control environments.
Besides the exported basic functions, the Java software allows (i) the display of a contextual GUI for the different working modes (puck loading or sample transfer), (ii) monitoring of critical parameters of the system, such as the gripper temperature or the dewar liquid-nitrogen level, (iii) the display and storage of log messages and activity results, (iv) the execution of image-processing tasks, (v) communication with peripheral devices (such as Datamatrix readers, ProxiSense control card, industrial cameras, 1-Wire IDs) and (vi) communication with other beamline devices (for example the goniometer). The movements of the robot arm and the time-critical processes (low-level machine control) are coded in the VAL3 programming language (Sta ¨ubli Faverges SCA, Faverges, France; https://www.
staubli. com/en/robotics/robotsoftware/val3-robot-programming/val-3-language/) and are executed in the CS8C robot controller. The communication between the high-level control and the low-level robotic control relies on the StaubCom library (EMBLEM Technology Transfer GmbH, Heidelberg, Germany; http:// software. embl-em. de) that exposes a generic VAL3 socket server on the network to provide a task-oriented programming environment. This low-level VAL3 software (i) dynamically loads and executes movement sequences, (ii) sends feedback messages to the high-level Java software, (iii) executes regulation loops, (iv) exposes IO states and commands to the Java software, (v) notifies the high-level control software of upcoming errors, warnings and log messages during the execution of low-level tasks and (vi) takes actions in case of errors and failures to preserve the integrity of the sample and maintain the operability of the machine for further tasks.
The software of the standard SmartMagnet controller card was upgraded to make it compatible with the SmartMagnetP. The magnetization command of the SmartMagnetP and the pin-presence detection signals were hardware-connected to the FlexED8 control electronics. Two different detection thresholds are used to reliably detect the presence of SPINE/SPINEplus and miniSPINE sample holders. The detection thresholds are changed manually using the control software of the MD2S, but can be further automated to make simultaneous use of miniSPINE and SPINE/SPINEplus sample holders possible. The NewPin goniometer mount was used to hold NewPin sample holders on the goniometer. No sample-holder detection was provided in this prototype goniometer mount.
Sample-transfer procedures All robot-movement sequences are written in the VAL3 programming language (PLC-IEC 61131-3 compliant) and are executed by the CS8C Sta ¨ubli robot controller running a VxWorks real-time operating system that ensures robust and reliable code execution. Various checks are performed during sample-loading and sample-unloading processes at different points of the gripper trajectory. If an abnormal situation is detected, decisions are automatically taken to preserve the integrity of the samples and keep the device available for further actions. A sample-recovery puck is installed in position 8 of the ED8 to store samples that cannot be mounted on the goniometer or replaced in their original puck. The robot grippers are parked in liquid nitrogen between successive sample-loading and sample-unloading cycles, and are deiced after 16 sample transfers.
In a typical loading or unloading procedure, the system performs the following checks: (i) when the gripper is in the vicinity of a puck, the system monitors the gripper collision-detection system and the ProxiSense puckpresence signal to detect any potential collision or removal of the puck from the dewar, (ii) during both loading and unloading procedures a pin-detection station placed in the middle of the robot trajectory and equipped with a machinevision system checks whether the pin is well positioned in the gripper and a Datamatrix reader simultaneously registers the sample-holder Datamatrix code (if any) and (iii) when removing or placing a sample on the goniometer the system monitors the gripper collision-detection signal and ensures that the signals 'cryo is back', 'transfer is authorized' and 'sample is present' provided by the goniometer are in the proper state to prevent any collision with the goniometer, sample holder or cryo-jet nozzle.
Tests with model protein Thaumatin was selected as a test protein. Crystals were grown by the hanging-drop method using a protein concentration of 15 mg ml À1 in a solution consisting of 100 mM N-(2acetamido)-2-iminodiacetic acid (ADA) pH 6. 5. The reservoir solution consisted of 100 mM ADA pH 6. 5 and 0. 9-1. 0 M sodium/potassium tartrate. Crystallization droplets consisting of 2 ml protein solution and 2 ml reservoir solution were equilibrated against 1 ml reservoir solution at room temperature. The crystals were manually harvested with the different types of sample holders and were briefly immersed ($2 s) in cryobuffer consisting of 0. 75 M sodium/potassium tartrate and 25%(v/v) glycerol before being cooled directly on the MD2S goniometer at 100 K. The diffraction data sets were collected at an energy of 13 keV with the long c unit-cell dimension of each crystal oriented along the spindle axis to avoid overlaps of reflections on the detector  (Dauter, 1999).
For NewPin sample holders, the same starting crystal orientation was easily achieved for each repeated data collection by using the automatic reorientation system of the NewPin goniometer mount  (Papp et al. , 2017). For the miniSPINE and SPINEplus sample holders, the initial crystal orientation was manually corrected using the MD2S goniometer. In order to expose the same crystal volume, data sets of 240 images with 0. 5 oscillation range were collected. The exposure time per frame was selected to ensure that no overloaded spots were recorded in test images taken 90 apart. The dose for data sets was estimated using RADDOSE-3D  (Zeldin et al. 2013 ) at 0. 42 MGy. This dose is well below the 'Garman limit' of 30 MGy  (Owen et al. 2006) , meaning that significant radiation damage should not occur in the tests performed here.
Five data sets were then collected successively. After each collection, the crystal was unloaded to the EdgeDewar and remounted onto the goniometer with a transfer time incrementally doubled for every successive transfer. The Wilson plots were then compared for each data set to show any changes in crystal quality and the appearance of crystalline ice (Fig. 7d ). After transfer at nominal speed and a second transfer with a 19/23 s unloading/ loading time, the Wilson plots did not show any significant deviation. After a transfer with 44/46 s loading/unloading times the Wilson plot clearly indicates that the crystal was losing its diffraction power as the intensity drops significantly. After a transfer with 85/90 s unloading/loading times the resolution dropped to 1. 8 A ˚. Crystalline ice rings also appeared on the diffraction images in the corresponding regions (for example at $3.
However, ice formation was clearly visible on the sample in the fourth data collection, and ice rings were observed on the diffraction images at the corresponding regions (for example at $3. 7 A resolution). These experiments show that for both grippers used at nominal speed the integrity of crystals is preserved with a comfortable safety margin. 3. 2. 2. Crystal preservation during successive transfers. The preservation of a crystal during multiple transfers is important as crystals are often screened for diffraction quality before a complete data set is collected from those displaying the best diffraction characteristics  (Bowler et al. , 2010). The effect of successive crystal transfers was evaluated for each sampleholder type with its corresponding gripper at nominal robot speed, using the following experimental protocol.
A thaumatin crystal manually mounted on the goniometer and flashcooled in the cryostream was cyclically unloaded/loaded to and from the EdgeDewar. Complete data sets were collected after the first, fifth and tenth transfer cycles. This experiment was repeated twice with each sample-holder model to ensure consistency of the results. For the first trial, the data sets were collected at 1. 25 A ˚resolution for the miniSPINE sample holders, 1. 34 A ˚resolution for NewPin sample holders and 1. 35 A ˚resolution for SPINEplus sample holders. For the second trial, the data sets were collected at 1. 25 A ˚resolution for the miniSPINE sample holders, 1. 25 A ˚resolution for NewPin sample holders and 1. 35 A ˚resolution for the SPINEplus sample holder. None of the samples displayed any deterioration in the diffraction limit, even after ten successive mount/unmount cycles.
Tests with Axe2 The miniSPINE sample holder is proposed as a near-future standard; therefore, supplementary tests were performed with samples provided by a user, which may not be as robust as thaumatin. Crystals of Axe2, an intracellular acetylxylooligosaccharide esterase from Geobacillus stearothermophilus, were used for this experiment. A similar data-collection procedure to that used for the thaumatin samples was adopted. Complete data sets were collected after the first, fifth and tenth transfer cycles at 2. 5 A ˚resolution in the first trial and 3. 3 A ˚resolution in the second trial. For each data set, a total of 120 images were collected with a 1 oscillation range. Exposure times of 5 and 4 s per image were used for the first and second trials, respectively. All data sets were processed using HKL-2000.
Acknowledgements We thank  Andrew McCarthy  for access to the BM14 beamline over five years and  Akim Khadrouche ,  Matias Guijarro  and  Antonia Beteva  for the integration of the FlexED8 sample changer in MXCuBE. We finally thank  EMBLEM Technology Transfer, Heidelberg, Germany  for managing the legal and commercial aspects of this project. The authors declare the following competing financial interest: EdgeDewar patent application No. CA 2879479  and patent Nos. EP 2 877 794 ,  US 2015/0211682  and  JP 2015-526686. The Flex robotics,  Edge-Dewar  and  FlexED8  sample changer, including the grippers, are commercialized by  Irelec-Alcen ,  Saint-Martin-d'He  `res, France and the grippers are commercialized by  NatX-ray, Saint Martin d'Heres, France , all under EMBLEM licenses (Heidelberg, Germany).
Our proposed algorithm is implemented in C++ and was evaluated on real data as part of the 2016 iDash Genome Privacy-Protection Challenge. Results: Results show that our solution outperforms the state-of-the-art solutions and enables researchers to search over millions of encrypted variants in a few seconds. Conclusions: As opposed to prior beliefs that sophisticated privacy-enhancing technologies (PETs) are unpractical for real operational settings, our solution demonstrates that, in the case of genomic data, PETs are very efficient enablers.
Naehrig et al. [10]  also show how certain approximated machine-learning algorithms can be securely run in the cloud by using HE. Finally, Wang et al. [11]  make use of HE to securely compute exact logistic regression. However, most of these solutions have had limited adoption because of either (i) their lack of flexibility (i. e. , some privacy-preserving solutions can be used only for specific tasks on specific types of data such as SNPs) or (ii) their cost (HE introduces a significant storage and computational overhead that substantially impairs scaling to real-size genomic datasets). In this paper, we address this problem by proposing a novel and very efficient solution for securely outsourcing genomic-data storage and processing that outperforms the state of the art. Our solution is based on HE and private information retrieval (PIR)  [12]  and enables a user to securely store millions of genomic variants of all types for one or multiple individuals on the cloud and to efficiently search for specific genomic variants without revealing anything to the cloud provider.
Due to its efficiency, the proposed solution was selected among the finalists at the 2016 iDash competition  [13] , which was held in Chicago, Illinois, USA on November 11, 2016. The iDash competition is a community-wide open competition whose goal is to bridge the gap between the biomedical informatics, data privacy, and security communities by benchmarking new secure solutions for known genomic-security problems. The intention is to address these issues and further advance the current state of the art in the genome privacy and security research field. We summarize the key contributions of this paper: • A new secure and efficient solution to storing and searching genomic data in a public cloud that provides data and query confidentiality and hides access patterns from the cloud. • The first application of private information retrieval for genomic data.
Background Our solution draws inspiration from a popular privacy technique known as private information retrieval. This protocol is of particular interest in a scenario when Fig. 1  Variant Call Format (VCF) file. This is a text file that stores genomic information, in particular, genetic variations, for example, single-nucleotide polymorphisms (SNPs). Each VCF file is divided into a header section, which provides some meta-data describing the remaining content of the file, and the body, which contains all the different variants one of the participating parties (e. g. , a server) owns a database and another party wants to perform one or more queries without leaking any information, such as access patterns. Conceptually speaking, we want to achieve the same level of privacy as we have when downloading the entire database from the server and performing the search locally.
There are many variations of this protocol, but the current model for our solution uses cPIR  [14]  (Computationally Private Information Retrieval) with HE, which safeguards against a polynomially-bounded querier. The way this protocol works, in practice, is to make each query indistinguishable and to ensure that the server processes all of its database entries. In this way, it cannot obtain any information, either by looking at the query or by looking at the computations performed on the database.
System model Our system comprises a data owner/client who possesses the genomic information in multiple VCF files. This information is sent to a cloud server to be stored in a database, as depicted in Fig. 2. The client can then query the server to find out if one or more variants are present in the data bank. This information can be used for multiple purposes and is particularly important for genome-wide association studies (GWAS) that statistically assess the correlation between genetic variants and disease status. Our solution is able to hide data, query and access patterns from the cloud. We envision simple scenarios such as a doctor who possesses the genomic data of his patients and wants to consult this information, or a patient who stores his genomic information on an external cloud service to free some space in his device.