dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence conservation Sequences of human lncRNA transcripts were downloaded from the GRCh38 Ensembl database, version 83. Sequence comparisons between our chicken FEELnc sequences and the human sequences were conducted using the Blast software suite  [74]  (blastn V2. 4. 0+, with a word size of 7). The thresholds used for the FEELnc and human transcript comparison were 50% for the query coverage and 70% for the identity percentage.
RT-qPCR Total RNA and cDNA were prepared from various tissues, as previously described by Roux et al. [80]. Four experimental bird designs were analyzed: 16 young males (9 weeks old) analyzed in this study with RNA-Seq data (FatInteger Project-ANR-11-SVS7), 56 adult laying hens (over 30 weeks of age) from the ChickStress Project-ANR-13-ADAP, 20 young males (9 weeks old) fed ad libitum or fasted for 16 h, and finally the multi-tissue design with 17 different tissues, as reported in Roux et al. [80]. RT-qPCR was performed with the primers included in Table  2 , and amplification specificity was confirmed by sequencing. The results are given either as CT (cycle threshold) or, for the multi-tissue design, as fold-change compared to a CT equal to 30 (considered as very weak expression).
Acknowledgements The authors thank the  INRA  staff at the breeding facilities ( Pôle d'Expérimentation Avicole de Tours, F-37380 Nouzilly, France ) for technical participation (UR83 Recherches Avicoles, F-37380 Nouzilly, France). English was improved by  Nature Research Editing Service from Springer Nature  (http://authorservices. springernature. com/language-editing/). Kévin Muret ,  Christophe Klopp ,  Diane Esquerré ,  Frédéric Lecerf ,  Hervé Acloque ,  Elisabetta Giuffra ,  Sarah Djebali ,  Sylvain Foissac ,  Thomas Derrien  and  Sandrine Lagarrigue  authors are partners of the  FAANG pilot project  ' Fr-AgENCODE '.
Full list of author information is available at the end of the article KM is a Ph. D. fellow supported by the  Brittany region (France)  and the  INRA's Animal Genetics division. SD was supported by the  Agreenskills fellowship program  which has received funding from the  EU's Seventh Framework Program  under Grant Agreement No  FP7-609398. The animal experimental designs and RNA-Seq data were funded by the  French National Agency of Research  ( FatInteger Project ,  ANR-11-SVS7  and  ChickStress  Project,  ANR-13-ADAP ) and by  Europe  ( Feed-a-Gene H2020  project).
g. stating a wrong allergy or wrong comorbidity); 0, if an item of information was unreported; 0. 5, if an item of information was partly communicated; 1, if an item of information was communicated correctly (e. g. correctly stating all the patient's allergies or comorbidities). The decision about which components were 472 Jullia et al. reasonably expected to be transmitted in the case of several components constituting one item (e. g. multiple allergies or comorbidities) was left to the discretion of the trained observer and generally aligned with documentation on the patient record. The maximum score possible was thus þ22 points. The data collection was done by two student nurse anaesthetist evaluators using their smartphones. Study data were collected and managed using Research Electronic Data Capture tools hosted at Digital Life/Paul Sabatier University.
• We bring novel and original insights on the planning of cloudlets for mobile access networks. By performing extensive simulations on real 4G cellular network datasets from the Île-de-France Orange network, we show the trade-off that can be achieved by means of the two design cases and the impact of user mobility on the cloudlet network: as few as 13 to 26 cloudlets can be planned for 180 thousands of users while requiring tight delay guarantees. We show that there is a sensible gain in the number of users with respected SLA, up to 20%, by including user and VM mobility in the network planning. We do also qualify the eligibility of two different VM mobility strategies, namely VM bulk and live migrations, for two reference mobile cloud services differing in the level of required latency and memory characteristics: augmented-reality and remote desktop.
• We report empirical distributions of the dataset features in order to allow the reproducibility of our results. In  [1]  we provide a preliminary modeling of the mobile edge cloud network design problem. In this paper we refine the model and we provide a new heuristic including a dynamic decomposition logic. Furthermore, we present a more detailed set of results, carrying out a substantially deeper analysis of policies and practices. The paper is organized as follows. Sect. II presents the background. Sect. III presents cloudlet network models and related mathematical formulations. Sect. V presents the dataset. Sect. VI reports experimental results and Sect. VII contains brief conclusions.
V. DATASET In order to ground our simulations on real data, we used a dataset collected by Orange mobile, France, in the frame of the ABCD project  [33]. The dataset comes from network management tickets, containing UE data exchange information aggregated in 6 minutes periods. User session is assigned to the cell identifier of the last used antenna. Data are recorded on a per-user basis and cover a large metropolitan area network, including urban, peri-urban and rural areas. We had access to data of a single 24-hour period, originated by 606 LTE 4G APs in an area of 931 km 2 , with a density of ∼ 0. 65 APs per km 2. The number of users served by the considered APs is ∼ 180 thousands, generating an overall daily traffic of 11T B. for each base station s ∈ B are drawn by direct queries from the dataset.
Instead of choosing a particular setting for C, U and D, we perform a parametric analysis on them, as presented in Sect. VI. 2) User Mobility Patterns: Individual user mobility patterns cannot be obtained for confidentiality reasons. Furthermore, allowing migrations even when an AP is visited infrequently would have a strong negative impact on the overall network load, without significantly improving user experience. Trying to cope with this issue we perform binning on data: for each user we consider his two more frequently visited APs during the planning horizon. We restrict to consider possible migrations only between these two locations representing, for  instance, home and work places of users, which, following  [36] ,  [37] , dominate human mobility. Technically, this data is obtained by creating groups of users and obfuscating individual identifiers.
VI. EXPERIMENTAL RESULTS We implemented our algorithms in C++, using IBM ILOG CPLEX 12. 6  [39]  to solve both LP and MILP problems. Our experiments ran on an Intel Core 2 Duo 3Ghz workstation equipped with 2GB of RAM. In a preliminary round, we experimented on a dataset of ten small size instances involving 50 APs adapted from the facility location literature  [31]. In these we could obtain valid lower bounds with our framework, measuring a worst case gap in terms of number of additional cloudlets activated in our solutions w. r. t. globally optimal ones. Our solutions were found to be of high accuracy: no extra cloudlets were activated in two cases, one extra cloudlet in seven cases and two extra cloudlets in the remaining case. That is, it was impossible to improve by removing more than a single cloudlet in nine over ten cases, even by assuming the most optimistic scenario.
We experimented on the real-world dataset, considering three cloudlet size cases: tiny cloudlet of C = 2 racks, car parking cloudlets of C = 4 racks and C = 5 racks, and a 2-4 DC-room cloudlet with C = 40 racks. Using values from  [14] , we assume one rack to host up to 1500 VMs. Considering bottleneck-free back-hauling networks (U ≤ 1), where latency is approximately directly proportional to the euclidean distances among nodes, we consider three latency bounds D: 'loose', 'mid-level', and 'strict' bounds, corresponding respectively to roughly the urban area radius (15 km), 4/5 of it, and 2/3 of it. These three levels of cloudlet access latency are chosen to correspond to three reference mobile cloud services: delay-tolerant storage box services for the loose case, delay-sensitive remote desktop services for the mid-level case, and delay-critical augmented-reality support services for the strict case.
For example, in Fig. 12(d ) we can see the cluster of APs associated to the nearest cloudlet using the same network used in Fig. 12 (b) and 12(c). As a first comparison, in Fig. 13  we show the percentage of users with violated SLA after a nearest cloudlet association. As compared with our approach in Fig. 11 , we remark that: (i) nearest cloudlet association produces SLA violations even with dynamic planning, while our approach guarantees no  As a second comparison, we compute the cloudlet overuse, i. e. the excess over VM capacity C. In Fig. 14  we present the overuse amount, noting that with 40-rack cloudlets there is no overuse, and with tiny 5-rack cloudlets there is a high overuse for several instances of both SP and DP approaches. c) Bulk migration results: Our initial attempts to optimize DP models with VM Bulk migration produced no feasible solutions on any instance of the dataset.
VII. CONCLUSION We provided for the first time at the state of the art a comprehensive mobile edge cloud network design framework for mobile access metropolitan area networks. We formally defined the problem, including two planning model variations: (i) considering a static status of the network, unaware of variations during the planning horizon, and (ii) considering a dynamic network, including load variations and mobility of users and virtual machines, encoding three different virtual machine mobility technologies. We compared the different planning options extensively for scenarii built over real cellular network datasets, differentiating between different traffic engineering and performance goals for reference mobile cloud services, analyzing: (i) the use of network facilities resources, i.
This mechanism allows us then to describe all of the AES encryption process in the form of files using the same representation as described above. So we have 128 files, one by bit of block. In these files, each line describes a monomial and the transition from one line to the next is done by the XOR operation. To implement this mechanism of the description of the AES encryption algorithm and generate the 128 files, we have developed and used a python script based on that described earlier in our presentation of AES 1. The main program, aes_equa. py, offers the possibility of one hand to generate the files for AES ciphering and deciphering functions with the generateEncFullFiles() and generateDecFullFiles() functions and on the other hand, to control that the encryption and the decryption obtained from files is consistent.
The parameters of these potentials can generally be trained on any set of structures of protein complexes by linear regression (LR) models or a directly scoring-dependent Monte-Carlo algorithm (MC). Thanks to pre-calculations of potential-specific feature vectors, re-scoring and re-evaluation of the whole benchmark can be performed very quickly by simple vector multiplications to facilitate the fast testing of several scoring functions and their combinations. As an application, we designed ten different scoring functions with four different potential forms in our newly defined grouped all atom (GAA) representation. We used unbound rigidbody protein-protein docking decoys obtained from the protein-protein docking program ATTRACT  [8]. The performance was evaluated on three different scoring problems on an independent test set consisting of 77 complexes.
most negative (A) and most positive (B) scoring contributions of the parameter (x-axis) for the LR_gaa_10 potential shown for the average native (blue), near-native (green) and incorrect (red) solution. The discriminating scoring contribution is defined as described in the methods section by the difference between the near-native and incorrect contributions (red and green). Tables A Protein data bank entries for training set of protein-protein complexes. Protein databank (pdb) entries of the training set consisting of 135 protein-protein complexes used for the parameter generation. Table B Protein data bank entries for test set of protein-protein complexes. Protein databank (pdb) entries of the test set consisting of 77 protein-protein complexes. Table C List of atom-types in the grouped-all atom (GAA) representation.
It includes hardware upgrades, control software extensions, external hardware-software control, and specialized Matlab signal processing algorithms. New analog input channels calibration procedures and methods were developed using Matlab tool. It is important to mention that for the first time the GEM detector (old version, tested and used in JET tokamak) was connected to the completely new version (in term of hardware, firmware and software) of the measurement system. Physical spectra at this stage of the project, were used in order to verify proper hardware construction, as well as performance of the signal processing path. The main goal of the project is to perform plasma tomography using two GEM cameras: horizontal and vertical. Each unit will produce two kinds of spectra: SXR energy spectrum and SXR topological spectrum  [10].
The digital section of the system located in the rack consists of new revision of Analog-to-Digital Converter boards (ADBs) connected to the backplane FPGA boards (Fig. 1 ). The backplane boards are then connected through the PCI-E switch to the embedded PC. Each new ADB can work with up to 16 input channels using 75-125 MHz sampling frequency. It is possible to perform offset calibration and modify the signal coupling (AC/DC). As a preprocessing unit, the Xilinx Artix7 FPGAs are used, located on the backplane boards. The communication with embedded PC is done through a PCI-E 8-to-1 switch using PCI-Express Gen3 interface (however the Artix7 FPGA implementation of the PCI-E core uses Gen2 interface). For data buffering purposes, onboard DDR3 memory is used. Complete system hardware architecture with 128 input channels from the GEM detector is presented in Fig.
). The new revision of firmware enables registration of up to 100k events per measurement cycle from the GEM detector in form of raw, analog signals from all 64 channels simultaneously. An example of the extracted raw signals from the system database is presented in Fig. 3. The analog pulses are correlated with one photon emitted by the X-ray source and registered by the GEM detector (electron cloud spreads over several GEM readout channels). Due to recording of raw ADC data, the Matlab-implemented post-processing software has access to the whole information available in the digitized signal. More details about overall firmware concepts, previous firmware versions and development of it can be found in  [9, 22, 23].
Software development and integration To perform data acquisition from the GEM detector and compute spectra: SXR energy spectrum and SXR topology spectrum, the system must be properly switched on, initialized and configured. Appropriate initialization and configuration is ensured by the FPGA Configuration Software (FCS)modular framework for fast, advanced measurement systems  [24]. The FCS uses drivers database for low-level communication interfaces, internal bus interfaces, and integrated circuits drivers. Its main function is to perform system initialization and configuration with automatic system self-check. It also performs data management (downloading, parsing, etc. ), providing an interface for user communication. To perform spectra computation, advanced algorithms need to be used, including cluster discrimination, charge computation and finally calculation of output products.
To correctly register charge signals from the GEM detector, the system needs to be tuned by complex algorithms. Those compute optimal offset level for all input channels of ADCs. The main goal of the offset correction is to achieve highest amplitude input signal range (from the GEM detector) on each channel by reducing the offset level. The algorithms are implemented in the Matlab scripts on external, independent PC. On this PC, data post-processing is done in order to compute final SXR spectra. It also works as a user front-end application, for measurement system user configuration. Connection to the FCS is provided by the recently developed TCP/IP server  [25]. The FCS communicates with the system through the PCI-Express and USB interfaces. In addition, there is a separate server application implemented for control of the HV power supply unit, necessary for proper operation of the GEM detector.
All of the applications are installed and are running on embedded highperformance server. The structure of the implemented software for the multichannel measurement system is presented in Fig. 4. The software (FCS and Matlab application) is highly modular. Therefore, the system can be further extended to support 128 input channels. It is also possible to easily narrow the number of input channels, limiting the system to 16 input channels without need of extensive software or firmware modifications. More details about concept and preliminary implementation of the FCS, as well as detailed post-processing algorithms description can be found in  [26] [27] [28].
Laboratory experiments After successful implementation and verification of firmware and software, first laboratory tests were performed. The measurements were done using one-dimensional GEM detector with 256 output channels (64 channels were used for tests). Ar:CO 2 gas mixture and high-voltage was provided to the detector. 55 Fe was used as an X-ray source, generating photons at the rate of approximately 1 kHz. With the source positioned at the center of the detector, very close to the window (around 1-2 cm), first spectra were computed. For the test purposes, 10 000 events were registered. The computed spectra are presented in Fig. 5. The number of counts presented in Fig. 5a  varies from 10 000 events due to Matlab data analysis algorithms (more events were detected in the data). In Fig.
Summary The paper presents status of extensive evolution and upgrade of the measurement system presented in  [9]. Significant effort was invested in order to integrate different parts of the system. The most important point was to implement control of the system through Matlab application, along with specialized spectra computing algorithms. Development was done in both firmware and software fields. For the first time GEM detector was connected to the system in order to gather physical signals from the X-ray source. Fast, multichannel preprocessing FPGA firmware with DDR3 memory and PCI-Express support was implemented and only functionally tested in extended version of 100 000 events registration. The spectra computation based on 55 Fe source, with mentioned amount of events, is planned to be done (the presented spectra are based on ∼10 000 events).
The FCS is used for the low-level system control. It was upgraded to handle multiple configuration parameters and integrated with the TCP/IP servers in order to provide user interface. Advanced spectra computing tools implemented in Matlab were successfully integrated with the system. The firmware for both the analog signal paths and data acquisition (64 input channels) was successfully verified. The first successful laboratory tests were performed with 64channel 1D GEM detector and 55 Fe X-ray source As the result, energy spectrum was produced, where one can distinguish Ar and Fe escape peaks. The second topological distribution has approximately flat shape which corresponds to physical location of the X-ray source. Those spectra confirms correctness of the development (firmware, software, integration with another tools) and system design.
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme2014-2018 under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. This scientific work was partly supported by  Polish Ministry of Science and Higher Education  within the framework of the scientific financial resources in the year 2016 allocated for the realization of the international co-financed project.
In many countries, the motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. To increase the safety of motorcyclists and minimize their road fatalities, this paper introduces a vision-based rear-end collision detection system. The binary road detection scheme contributes significantly to reduce the negative false detections and helps to achieve reliable results even though shadows and different lane markers are present on the road. The methodology is based on Harris corner detection and Hough transform. To validate this methodology, two types of dataset are used: (1) self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and (  2 ) online datasets (recorded by placing a camera at the front of a car).
They are also sensitive to camera movement and may fail to detect objects with slow relative motion. 31 As such, motion-based techniques are less commonly used for vehicle detections. On the other hand, appearance-based detection techniques detect vehicles based on shadow underneath the vehicles,  32  color,  33  symmetry,  27, 31  texture,  34  lights,  35  and edges. 36 n this paper, a rear-end vision-based collision detection system has been proposed for motorcyclists. This system detects incoming vehicles from the rear-end using a single camera; it has been tested on different road scenarios and on available online datasets to evaluate its performances.
Related Work Recent related works for vehicle detections are given in Table  1. This table covers the detection type and the dataset properties; it also highlights some results through metrics, such as accuracy and true positive rate (TPR), and it further provides the limitation of these techniques. In Table  1 , the "detection type" column defines whether the technique is applied either for the front or rear-end vehicle detection. The "dataset" column provides information related to the videos, the number of images, or the type of vehicles involved in the experiments. In Ref. 32, a single camera was used to detect front vehicles. The technique uses a combination of two features namely the shadow underneath the vehicle and horizontal edges for vehicle detection; the analysis of consecutive frames is used to calculate the relative speed of the detected car.
Data Set In this research, a Sony action cam was installed at the rearend of a motorcycle (Yamaha, 115cc) to acquire the dataset    as shown in Fig. 1. The camera was mounted in a shocked proof casing to minimize any vibration effects. The video datasets were recorded in ". avi" format at 30 frames per second and at a resolution of 600 × 800 pixels. A total of 5000 frames containing 120 vehicles were used for testing. The video datasets were recorded in different road scenarios having different light conditions. During the video recordings, the driving speed of the motorcycle varied from 40 to 80 km∕h. The datasets were recorded along Ipoh-Lumut highway, Perak, Malaysia and inside Universiti Teknologi PETRONAS (UTP), Malaysia. 4] [45]  The "source-2" dataset is available at Ref. 46. The details of the datasets are given in Table  2.