dataesr/scientific-paragraphs-categorization huggingface dataset:
Automated data processing and analysis Modern SAXS beamlines are capable of producing large amounts of data and automated processing and analysis is nowadays a necessary prerequisite for an efficient beamline operation. The first prototype pipeline encompassing the entire data analysis process was developed for the X33 beamline  (Franke et al. , 2012). On P12, a full data analysis pipeline (SASFLOW), starting from handling two-dimensional scattering patterns and ending with the construction of the ab initio low-resolution particle shape, is implemented, which is a further development of the prototype reported by  Franke et al. (2012). Another major advantage of the modular setup of SASFLOW is the possibility to easily add additional components to the pipeline for specific experiment types.
For example, a number of customized modules including buffer recognition and correlation of biophysical data with SAXS data were added for the on-the-fly data processing and analysis of the on-line chromatography SEC-SAXS (TDA) experiments. The automated analysis using the SEC-SAXS data in combination with TDA is still a work in progress. Below we shall briefly present the main features of SASFLOW, highlighting the recent developments made when implementing the pipeline to P12 (Fig. 11 ). The outcome of a SAXS measurement is a two-dimensional scattering pattern, which is radially averaged and normalized against the transmitted beam intensity. The radial averaging previously was done by the BMS and tailored to the particular setup; at P12, it has been made hardware independent. This allows reproduction of the radial averaging step by the user.
Exposure of a specimen results in a number of frames (typically 20), which are compared to detect radiation damage using a novel approach called correlation map  (Franke et al. , submitted). The frames that do not have any statistically significant differences are automatically averaged to improve the signal-to-noise ratio. Typically a buffer is measured before and after each sample. The buffer background is subtracted from the sample signal, followed by normalization by the sample concentration. Further analysis of the subtracted data includes evaluation of the radius of gyration (R g ) and the forward scattering I(0) from the Guinier approximation  (Guinier & Fournet, 1955). The forward scattering I(0) as well as the Porod volume are used to estimate the molecular weight of the macromolecule.
Currently, molecular weights are determined from the forward scattering value using a calibration standard (bovine serum albumin). Automatic absolute scaling, using the scattering of pure water and of the empty capillary, is in development and will be available in the near future. The pair distance distribution function p(r) is automatically computed and provides another estimate for the radius of gyration and the maximum dimension of the particle, D max. The particle shape is then automatically reconstructed ab initio using DAMMIF  (Franke & Svergun, 2009). The derived overall parameters are stored in an XML summary file, which can be visualized by a web browser (Fig. 11b ). To provide real-time visual feedback a number of plots have been included into the P12 summary. The experi-mental data are plotted as log I(s) versus s.
A robust and efficient automation of the data collection and handling allows for SAXS data collection and analysis with minimal intervention from the experimentalist with the preliminary results provided within a few minutes. With this automation not only can the experimentalists measure many more samples during a visit to the synchrotron, but the direct feedback provided by the data analysis pipeline allows for a more interactive approach, e. g. determining that a sample needs to be re-measured or its buffer conditions must be adjusted. Moreover, mail-in and remote operation are routinely offered to users. For mail-in experiments, the samples sent by the users are measured by the beamline staff, and the raw and processed data and preliminary ab initio models are sent back to the user. For remote operation, the samples sent by the user are placed in the sample changer storage tray by the beamline staff.
The user takes full control of the measurements over the internet using the free NX client of NoMachine (http://www. nomachine. com), as described by  Franke et al. (2012) , and the data collection is performed from home or office (for more information see: http://www. embl-hamburg. de/biosaxs/user_ info. html). The instrumental setup presented here fulfils the expectations of most biological SAXS users but the instrument is of course being further developed. Thus, an environment for SAXS experiments in extreme conditions (chemical, temperature, pressure) is being prepared in collaboration with HZG. Among the ongoing developments at P12 are the extended possibilities for rapid kinetic experiments, which will be accessible as of spring 2015 thanks to the multilayer monochromator (presently being installed) that will boost the flux by a factor of 50.
To allow for a sub-microsecond range time resolution, appropriate devices are being developed to produce and control precise X-ray pulses, to synchronize the measurements, and to achieve a rapid triggering of the biological reactions. The use of a microfluidics chip on the SAXS beamline is also being further developed. For the screening chip, automatic alignment of the well with the beam is being implemented to allow full automation of the data collection. This work was supported by the European Community's Seventh Framework Programme under BioStruct-X (grant agreement No. 283570), Bundesministerium fu ¨r Bildung und Forschung (BMBF) projects 'Synchrotronstrahlfuehrungen fu ¨r strukturbiologische Forschung am PETRA III Speicherring' (05ES7YE1), BioSCAT (05K12YE1) and SAXS-CD (05K10VFB), Human Frontier Science Program (RGP0017/ 2012), and the EMBL Interdisciplinary Postdoc Programme (EIPOD, to MAG).
Data set collection The sequences of the 57 selected genes from strain 9231-Abomsa, including flanking regions (up to 350 bp), were concatenated and annotated, resulting in an "enlarged" sequence of 107 bp (Additional file 3). This sequence was used as reference to automatically retrieve the entire gene set from the whole genome sequence data of 21 Mccp strains by mapping raw data using Seqman NGen (2. 0) software (DNASTAR, Madison WI, USA). First, this procedure allowed the correct mapping of reads on the entire sequence of each corresponding gene, thanks to the presence of flanking regions. Second, it allowed the visual verification of sequencing depth and the identification of any incongruities on all coding sequences in the Seqman genome browser. On average, a read depth of 500X was obtained.
SNPs and indels were called when more than 85% of the reads supported the change. An in-house software was developed at CIRAD Montpellier to extract and concatenate the gene sequences corresponding to each strain from the enlarged reference sequence. Sequence searches are based on the Needleman-Wunsch algorithm, using tags to frame sequences of interest. These tags are short sequences homologous to the extremities of each target gene. The algorithm described here is implemented in C++ as a stand-alone program, "SelectRegion", and the source is freely available from the authors on request. The process can also be automated using graphical-interfaces within the web-based Galaxy  [27]. Otherwise, sequence data were retrieved from the published genome sequences of strains M1601, 99108 and ILRI181 (Table  1 ).
Phylogeny and molecular dating analyses From the 57 genes initially selected, a subset of 47 coding sequences was retained for evolutionary analyses. The 47 genes of 25 Mccp strains were extracted from each corresponding enlarged reference sequence using the internal software SelectRegion. The type strain of the subspecies Mcc (California Kid T ), which is the closest relative of Mccp, was chosen as outgroup and corresponding sequence data were obtained from the published genome sequence [Genbank:CP000123]. Sequences were aligned using Clustal W with default parameters (Additional file 5). The Tamura-Nei, 93 (TN93) model was selected as the best fitting model by Modeltest V3. 7  [32]. A maximum-likelihood phylogenetic tree was inferred using PhyML V3. 0  [33]  on the Galaxy web platform. A bootstrap resampling procedure with 1000 replicates was used to assess the reliability of key tree nodes.
To infer a temporal framework from dated sequences, a Bayesian approach, implemented in the flexible Bayesian phylogenetic analysis package BEAST V1. 6. 2  [34] , was used. This allowed the simultaneous estimation of the tree structure, the time of the most recent common ancestor (MRCA), the divergence time of nodes, and the mutation rate. The TN93 evolution model and partition codon (1 + 2, 3) were selected. Two molecular clocks (strict-clock and uncorrelated lognormal-clock) and various demographic models (constant, expansion, exponential and extended Bayesian skyline plot) were tested. Convergence was evaluated using Tracer V1. 6. To choose the best fitting clock and demographic model, the Bayes Factors  [35, 36]  were calculated from the marginal likelihoods on the Akaike's information criterion through MCMC (AICM) using Tracer V1.
Evolutionary history of Mccp A robust phylogeny of Mccp was reconstructed based on high-throughput genomic data of 25 selected Mccp strains (Table  1 ) and an Mcc outgroup. Among the 57 genes previously analysed for genotyping, a set of 47 coding sequences was retained, while pseudogenes were excluded (Additional file 1) to minimise molecular clock variation and homoplasy. After alignment of the sequences, robust trees were inferred based on 134 SNPs using both maximum likelihood (Additional file 7) and Bayesian (Figure  2 ) approaches. Tree topologies were identical for supported branches. The six genotyping groups described above (group A to group F) were retrieved as clusters supported by high node support values. They formed two major lineages. Lineage I included group A, present in East Africa, and group B, present in Central Africa.
Lineage II comprised groups C, D, E and F and was divided into two sub-lineages. The first sub-lineage comprised groups C and D, representing Asian strains, while group E (representing strains from the Mediterranean Basin) and group F (comprising mainly strains from East Africa) were clustered in a second, heterogeneous sub-lineage. Divergence time for Mccp strains was estimated from the dated sequences using the flexible Bayesian phylogenetic analysis package BEAST (Figure  2 ). Bayes Factor tests indicated that the strict-clock model fitted the data better than the relaxed-clock model. In contrast, there was no substantial difference between population models. To exempt analyses from dependence on a pre-specified demographic model, the extended Bayesian skyline plot model was applied. The mean substitution rate for Mccp, estimated on 47 coding sequences, was 1.
Initial context comprehension We conducted a field study to investigate the caring needs of residents of geriatric centers, and to learn the methods and procedures used for the care of older adults. The field study took place in three different institutions in the state of Baja California in the northwest of Mexico, two of them private and one public. We conducted a non-participatory observation (180 hours), and a total of 19 semi structured interviews with caregivers and residents of the three institutions. Residents came from different socioeconomic strata; had different levels of support from their social network and also variable levels of health and physical and mental fitness. This implies a variety of specialized care needs to be met on an individual basis. From the data gathered, we performed an analysis to obtain a classification of the main activities of both the residents and the caregivers.
Prototype 2. SMAMI. Purpose. The principal objective of SMAMI was to monitor residents whereabouts and inform caregivers in real-time. Functional services. This prototype was designed for continuously monitoring residents' mobility, to determine their location, and for sending real-time notifications to caregivers. The data from location sensors and accelerometers were collected in a central server. From the collected data it was possible to detect risky locations (Insight #2, Technical requirement #2. 1) and from the motion sensors data, a possible fall can be detected (Technical requirement #2. 2, Insight #3, and Technical requirement #3. 1). A Wireless Sensor Network (WSN) is part of SMAMI. A large number of fixed sensors (fixed nodes) need to be installed in strategic areas of the residence to sense residents.
7 ). The notifications service works with mobility, places, duration and the hour of the day. Also, a decision tree  (Rokach & Maimon 2008)  is used to decide when to send notifications and inform caregivers about the risky situations. An android application was developed to show to the caregivers the location of the residents in real time (Fig. 8a ), and also to alert them when a dangerous situation occurred (Fig. 8b ). The notifications should be provided just in time, because time is an important element when a fall occurs, for example. Non-functional services. The data from location sensors and accelerometers was collected in a central server and a mechanism to improve the accuracy of the locations in real-time was developed. The accelerometer data was fed to a process recognizing postures and launching notifications.
Evaluation Both prototypes were evaluated in one of the residences during four weeks. They were used daily by the caregivers for five months after de evaluation. Fig. 9  illustrates residents wearing sensors and caregivers using the mobile application. The evaluation of the prototypes was conducted in one of the three geriatric homes. The in-situ evaluation was designed to measure the efficiency and effectiveness by monitoring 12 residents, the perception of use of the applications and their usefulness was evaluated with 11 caregivers. Furthermore, we assessed the effectiveness of the notifications that served as alarms and reminders for the caregivers. The acquired and reported data were corroborated by means of 12 hours of observation, 11 interviews and 2 focus groups. This evaluation provided complementary information on some aspects not covered in detail in the initial design of the prototypes.
Insight #13. A mechanism to avoid the constantly text input by caregivers is needed. Technical requirement #13. 1. The system need to be design to diminish the caregiver constant registering. Even if the systems aimed at helping caregivers the perceptions were disparate about the usefulness of the system. One opinion was "It is easier than searching in paper" [Caregiver 11], while others did not perceive a substantial advantage between paper and electronic logging. "It is more difficult for me (the system usage) because in the notebook (referring to the paper logs) it was just writing, it is essentially the same, but using technology"  [Caregiver 8]. By means of the in-situ evaluation we collected data about the residents and caregivers. The data recorded during the evaluation consisted of the activity logs of the residents, notes from caregivers, location and accelerometers from wearable and environmental sensors.
This module is also responsible for data reliability, with subroutines handling communication protocols, such as CoAP Application Protocol) and MQTT (Message Queuing Telemetry Transport) which have more tolerance to communication faults and latency. Uninterruptable power sources are provided to support static sensing infrastructure. Caregivers will continue registering residents' activities. The data repository will store both: activities logs and sensor data. The inference engine will take the data from the repository and process it to provide structure. The data fusion will mix the data sources to analyze and discover patterns in the activities. The patterns found will be stored in the patterns repository. Additionally, the data management and control module should be supervised by the privacy policies to enforce physical devices and personnel to respect the privacy agreement of the data.
The data collected from the in-situ evaluation of the prototypes was manually mined and processed to find usual and unusual patterns in the states of each resident. For instance, if one day the caregiver observes that the resident is eating breakfast before someone read his vital signs, it would be an unusual pattern because it is mandatory for a caregiver to take the resident's vitals immediately after he gets out of bed. Another example is when a resident has the same succession of states, day after day, during a couple of months; then suddenly the resident changes something in his routine, the schedule should be capable of detecting this deviation.
It is worth noticing that after inspection of the data collection we went with the most reliable reading, either a sensor or a human assessment. The above assignment should be done by the data fusion manager and the inference engine. Most events of the resident's routine were logged using the prototypes described. We have grouped the events by semantic similarity and obtained a transition matrix. This arrangement, although simplistic, is quite informative. The activity logs of 14 subjects (residents marked as S1 to S14) were processed to obtain the transitions between states, and we counted the number of times one state follows another. In this process data were split into 8 hours chunks depending on the timestamp. The first chunk includes activities between 6 a. m. and 2 p. m. , the second between 2 p.
ASHYI-EDU To validate ASHYI, the framework was instantiated into a specific domain (education). The result is ASHYI-EDU, a dynamic adaptive planning system for virtual learning environments. ASHYI-EDU is a Java-based application that enhances Sakai (Apereo-Foundation, 2014), a virtual learning environment software, to provide personalized education to students. ASHYI-EDU addresses several issues in the teaching-learning process. First, teachers need to develop plans that convey knowledge, abilities, and competencies to students. Those plans comprise several learning activities that students must perform to reach specific learning goals in a course. Since students are heterogeneous, they may benefit more from some type of activities and less from others. For instance, some students prefer to learn by executing practical activities, while others would learn better by doing theory-intensive activities.
CSEDU2015-7thInternationalConferenceonComputerSupportedEducation A prototype of ASHYI-EDU was created, which utilizes Sakai (Apereo-Foundation, 2014), a Javabased virtual learning environment software. The implementation of ASHYI-EDU relies on a modified version of Sakai's Lesson Builder component that includes dynamic activity planning to build learning units and its lessons and a simple interaction facility for students and teachers. The development of the online course focuses on automatically performing several tasks that were slow or cumbersome in the offline course: i) providing adapted plans according to the necessities of each student, ii) automatically re-planning according to context changes, and iii) register the interaction between users and the system. The ASHYI-EDU prototype provides several features to facilitate the creation of dynamic adaptive plans and monitor their execution.
The proof checker A direct and natural way to implement the FPC approach to proof checking is to use a logic programming language: by turning the augmented inference rules sideways, one gets logic programming clauses. In this way, the rule's conclusion becomes the head of the clause and the rule's premises become the body of the clause. When proof checking in (either classical or intuitionistic) first-order logic without fixed points, the λ Prolog programming language  [10]  is a good choice since its treatment of bindings allows for elegant and effective implementations of first-order quantification in formulas and of eigenvariables in proofs  [9]. When the logic itself contains fixed points, as is the case in this paper, λ Prolog is no longer a good choice: instead, a stronger logic that incorporates aspects of the closed world assumption is needed.
In particular, the ACheck system has two parts. The first is a theorem prover; we have used Abella since it was easy to modify it for exporting theorems and certificates for use by the second part. The second part is the proof checker, FPCcheck, that verifies the output of a session of the theorem prover, suitably translated. This checker is written in the Bedwyr model checking system  [5]  and is the new component underlying this particular paper: its code is available from https://github. com/proofcert/fpccheck. The documentation at that address explains where to find Bedwyr and our modified Abella system. To illustrate here the kinds of examples available on the web page, the Abella theory files can have a ship command that is followed by a string describing a certificate to use to prove the proposed theorem: the checking of this certificate is shipped to the Bedwyr-based kernel for checking.
In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.
We propose a novel and generic approach for 3D scene analysis which relies on individually optimized 3D neighborhoods for both feature extraction and contextual classification. Considering different neighborhood definitions as the basis for feature extraction, we use a Conditional Random Field (CRF)  (Lafferty et al. , 2001)  for contextual classification and compare the respective classification results with those obtained when using a Random Forest classifier  (Breiman, 2001). As the unary terms of the CRF are also based on a Random Forest classifier, we can quantify the influence of the context model on the classification results. After reflecting related work in Section 2, we explain the different components of our methodology in Section 3. Subsequently, in Section 4, we evaluate the proposed methodology on a labeled point cloud dataset representing an urban environment and discuss the derived results.
e. either a radius or k), which is commonly selected to be identical for all 3D points and determined via heuristic or empiric knowledge on the scene. As a result, the derived scale parameter is specific for each dataset. In order to obtain a solution taking into account that the selection of a scale parameter depends on the local 3D structure as well as the local point density, an individual neighborhood size can be determined for each 3D point. In this context, most approaches rely on a neighborhood consisting of the k nearest neighbors and thus focus on optimizing k for each individual 3D point. This optimization may for instance be based on the local surface variation  (Pauly et al. , 2003; Belton and Lichti, 2006) , iterative schemes relating neighborhood size to curvature, point density and noise of normal estimation  (Mitra and Nguyen, 2003; Lalonde et al.