dataesr/scientific-paragraphs-categorization huggingface dataset:
To account for the harsh ocean conditions, an analysis is completed to ensure that the boat is self righting. Further, as is explained, the configuration of the sail, rigging and rudder is intended to reduce energy consumption. To extend the mission time, an efficient controller is essential. The integration of the controller includes a choice of sensors and actuators as well as the programming of a low-complexity navigation on a low-power processor. In  [13] , the sensors and actuators were taken into consideration to optimize the vessel performance. A data logger stored timestamped information on sensor readings as well as information about the working of the system. This allowed the performance and behavior of the system to be studied post testing. Similarly, in  [14] , a simulation of the vessel controller was performed in Matlab.
Mechanical Design This section describes a robust mechanical design for a small autonomous sailboat intended to cross the Atlantic autonomously. To assess navigation ability in a wide range of conditions, the heel angle is predicted as a function of wind speed. Furthermore, the motor control system is described to minimize energy consumption. Finally, the boat fabrication process is summarized. In order to determine the vessel's angle of heel at sea, both the experienced wind and the boat's resistance to heeling must be known. Pilot charts with average monthly wind information are available for the Atlantic (https://msi. nga. mil/Publications/APC, accessed on 29 August 2021), outlining the expected range of wind speeds for a given course over a reasonable time frame. However, almost all weather data are taken in freely moving air 10 m off the water surface.
Real-Time Controller In this section, first, a choice of low-power sensors is justified; then, the embedded processor software architecture is described; finally, the navigation algorithm is detailed. In order to navigate, autonomous sailboats require a number of sensors, specifically a Global Positioning System (GPS) sensor to provide real-time position, a compass that provides vessel heading, and a wind vane that returns the wind direction. The physical sensors for the SeaLeon were the same models as for the Breizh Tigresse, since they were chosen very carefully to minimize power consumption, as is reported in Section 4. 2. Specifically, the GPS was a breakout board distributed by Adafruit; it includes an onboard patch antenna, consumes extremely low power, and provides high sensitivity.
It includes the GSM MTK3339 chipset and is interfaced to the controller using a USART (Universal Synchronous and Asynchronous Receiver and Transmitter). The compass was the HMC6343 by Honeywell, and provides a three-axis magneto-resistive sensor. It is interfaced to the controller using an I2C interface. Both the GPS and compass were strategically mounted inside the hull of the SeaLeon. A CV7 windvane manufactured by LCF uses ultra-sonic measurement to provide a reliable estimate of the wind direction. It was mounted on a short mast at the back of the hull, and was connected to the controller through a USART interface. Additionally to the three sensors, the SeaLeon used an XBee Series 1 radio module for testing purposes, as well as the XEOS Onyx Iridium Asset Tracker to track and transmit the location of the sailboat.
During the calibration, angle readings were taken with the compass while the compass was turned in each 30 degree increment. The average angle was taken from three trials and compared to the actual angle, and the output served to populate a lookup table. From this information, the magnetic deviation could be calculated and was included in the code to obtain the heading with respect to true north. The final correction also included a correction that accounts for the magnetic declination between the true north and the measured north from the compass φ c , which is a function of the longitude L in degrees and can be estimated to be equal to φ c = 0. 3L -1. 3 in the North Atlantic Ocean. The SeaLeon used one microcontroller to receive sensor data, control the motors for navigation, and communicate its location.
The software implemented on the microcontroller is modular and has a hierarchical design with three layers: an input/output layer, a task layer, and a control layer. A hierarchical design was chosen because of its adaptability, due to the separation of hardware specific drivers from high-level logic. Specifically, this architecture allows the easy integration of new devices, since the same control layer can be used, and only the input/output layer should be updated. The input/output layer includes device drivers for all of the peripherals used for the SeaLeon. These include the USART, the I2C bus, the analog-to-digital converter (ADC), and the pulse width modulation (PWM) generator. The task layer contains the high-level modules that control the peripherals. The control layer is responsible for managing the state of the sailboat and performing tasks at periodic intervals.
To handle the multiple tasks, a clock interrupt handler expires every 200 milliseconds. When the handler is triggered, custom coded tasks are called, each at a unique period, as summarized in Table  2. This allows the controller to read the sensors, log the data, and control the motors at a pre-configured rate. In a future software design, the tasks running on the controller can be defined using a real-time operating system such as Free RTOS that is compatible with the ARM processor chosen. This can identify different levels of priorities for the tasks, and can be programmed to sleep to further reduce the energy consumption of the microcontroller. The sailboat has two data logging modes: test and deploy. In test mode, the data are sent using an Xbee Series 1 radio module. The data can then be visualized in real time to a host computer for processing.
Additionally, this last option avoided the marine traffic from Halifax. Both routes meet off Sable Island at the edge of the continental shelf. Then, both routes follow the north option depicted in Figure  5  and described in Section 5. 2. Off the Grand Banks of Newfoundland, the route follows the great circle route up to a target point located on the finish line in the west of Brest, France. Weather routing aims to define the optimal route and to estimate the energy budget needed to complete the Microtransat Challenge  [28]. Since the maximum forecast prediction period available is 16 days, it is necessary that the rest of the journey is predicted using pilot charts. The simulation was performed with Opencpn software. This software includes a simple Velocity Prediction Program (VPP) that gives a rough assessment of our design.
Conclusions At Dalhousie University, the Faculty of Engineering launched a very successful program to design a small autonomous sailboat to cross the Atlantic, as part of the Microtransat Challenge. The project is currently led by engineering students, who are learning from experience how to optimize a sailboat for a long-term mission. Specifically, a robust me-chanical structure, as well as the design of a controller for minimum energy draw, are taken into consideration. In this paper, the focus is on a comparison between the SeaLeon and her predecessor, the Breizh Tigresse. The SeaLeon's hull was inspired by a cutter-rigged Bermudan yawl, and its behavior at scale was analyzed using CAD tools. The sail and rig were chosen to represent that of a traditional human operated sailboat, and the navigation control system allowed for an energy-efficient management of the actuators.
Acknowledgments: We would like to thank  XEOS , the  Dalhousie Faculty of Engineering , including former  Dean Josh Leon ,  Jim Hanlon  and friends at  COVE ,  Rob Warner  and  Piotr Kawalec , the various students who supported the design (namely  Andrew Dobbin ,  Thomas Gwynne-Timothy ,  Julia Sarty ,  Anthony Chalmers ,  Serge Toutsenko ,  Blake Meech , and  Garrett Jenkins ) as well as MAST led by  Jade Farr  for technical and financial support throughout the lifetime of this project.
In this study, we used next-generation sequencing technology to generate a comprehensive transcriptome database encompassing human in vitro PCD. Analyses of differentially expressed genes during PCD revealed 6,374 genes that we organized into clusters of coexpressed genes based on temporal expression patterns. The major temporal programs we identified were associated with key pathways consistent with PC biology, as well as novel pathways with potential importance in PC differentiation. Additionally, our analysis revealed 449 transcriptional regulators correlated with these temporal programs of gene expression. Novel transcription regulators with consistent and marked overexpression during PCD include BATF2, BHLHA15/MIST1, EZH2, WHSC1/MMSET and BLM. Furthermore, our analysis identified many epigenetic actors upregulated at preplasmablast stage, a critical step where cell proliferation is high and where immunoglobulin secretion starts.
RNA sequencing and data analysis. The RNA sequencing (RNA-seq) library preparation was done with 150ng of input RNA using the Illumina TruSeq Stranded mRNA Library Prep Kit. Paired-end RNAseq were performed with illumina NextSeq sequencing instrument (Helixio, Clermont-Ferrand, France). RNA-seq read pairs were mapped to the reference human GRCh37 genome using the STAR aligner  14. All statistical analyses were performed with the statistics software R (version 3. 2. 3; available from https://www. r-project. org) and R packages developed by BioConductor project (available from https://www. bioconductor. org)  15. The expression level of each gene was summarized and normalized using the DESeq2 R/Bioconductor package  16. A summary of read mapping and quantification results can be found in Supplementary Figure  S2.
The RNA-seq data are available in Gene Expression Omnibus under the accession number GSE148924. The raw gene-wise read counts are provided in Supplementary File 1. Differential expression analyses were performed using DESeq2 pipeline. P values were adjusted to control the global FDR across all comparisons with the default option of the DESeq2 package. Genes were filtered from downstream analysis if they did not have a log2 mean normalized count value of at least 6 in at least one group. Genes were considered differentially expressed if they had an adjusted p-value less than 0. 05 and a fold change greater than 2. Heatmaps of gene expression were generated using the ComplexHeatmap R/Bioconductor package. Pathway enrichment analyses were performed using the R package ReactomePA  17.
Human Myeloma Cell Lines (HMCLs) XG human myeloma cell lines were obtained as previously described  18. JJN3 was kindly provided by Dr. Van Riet (Brussels, Belgium), JIM3 by Dr. MacLennan (Birmingham, UK), and MM1S by Dr. S. Rosen (Chicago, USA). AMO-1, LP1, L363, U266, OPM2, and SKMM2 were purchased from DSMZ (Braunsweig, Germany) and RPMI8226 from ATTC (Rockville, MD, USA). All HMCLs derived in our laboratory were cultured in the presence of recombinant IL-6. HMCLs were authenticated according to their short tandem repeat profiling and their gene expression profiling using Affymetrix U133 plus 2. 0 microarrays deposited in the ArrayExpress public database under accession numbers E-TABM-937 and E-TABM-1088  18.
RNA-Seq profiling of in vitro human PC differentiation To obtain a global transcriptomic map of human PCD, we performed RNA-seq analysis of four in-vitro human PCD subpopulations: memory B cells (MBCs), preplasmablasts (prePBs), plasmablasts (PBs) and plasma cells (PCs)  3, 4. ~50 million read pairs were generated for each RNA sample. The number of mapped reads per sample is provided in Supplementary Figure  S2. First, we determined the proportion of mapped reads per transcript classification in each cell subpopulation (Figure  1A ), based on Ensembl gene biotype annotation model. As expected, PCD is accompanied by a gradual increase of immunoglobulin gene expression. This increase starts from prePB stage and becomes more pronounced at PB and PC stages. We wondered if the strong expression of immunoglobulin genes by PBs and PCs could restrict the expression profile of other genes.
To better understand the gene expression changes occurring at each transition stage, we used the ReactomePA R/Bioconductor package to determine enriched molecular pathways (Supplementary Table  S2 ). The top 20 significantly enriched pathways are shown for each PCD transition (supplementary Figure  S4 ). Consistent with known biology, the transition from MBC to prePB is mainly characterized by the activation of cell cycle pathways. The transition from prePB to PB is marked by a strong downregulation of cell cycle genes (Supplementary Figure  S4B ). Overexpressed genes during prePBs to PBs transition are enriched in genes involved in protein production with increased metabolic activity (translation, modification, transport, unfolded protein response and chaperones) (supplementary Figure  S4A ), underlying the increasing immunoglobulin-secreting capacity.
Identification of temporal gene expression patterns during human PC differentiation Differentiation processes require expression changes and can therefore be accompanied by at least four general temporal gene expression patterns  21  that we refer to as one-step-up or one-step-down (mRNA level transitions from low to high or high to low, respectively, in two consecutive differentiation stages) and two-step-updown or two-step-down-up (i. e. impulse-down) (mRNA level transitions from low to high and back down or from high to low and back up, respectively, in a series of differentiation stages) (Figure  3A ). We built a R package named stepprofiler (https://github. com/kassambara/stepprofiler) to extract the temporal gene expression patterns of human PCD. This analysis identified 8,419 genes (Supplementary Table  S3 ) with one or two transition points in expression during PCD (Figure  3  and Supplementary Figure  S5 ).
The process gg → gγγ contains the most complicated non-planar topologies with up to rank five tensor numerators even at leading colour. Diphoton production has been known at NNLO for some time  [66, 67]  and the two-loop scattering amplitudes were among the first complete 2 → 2 process to be calculated  [68, 69]. The first results for the amplitudes for pp → γγj appeared in the last few months both for the amplitudes  [70, 71]  and NNLO differential cross section  [15]  in the leading-colour approximation. Very recently, the full-colour two-loop QCD corrections for the quarkinitiated channels to pp → γγj were presented  [72]. We obtain sufficiently compact analytic expressions for the complete set of helicity amplitudes for which the ultraviolet (UV) and infrared (IR) poles have been subtracted, and implement them into an efficient and stable C++ code as part of the NJet library  [73].
In particular, we show explicitly some remarkably simple analytic forms we obtained for the all-plus helicity amplitude, which highlight its conformal properties. Finally, we present the implementation in the NJet library  [73]  and the performance of the code using a realistic set of phase-space points before concluding with a few remarks on future applications of the results and methods. We also include an appendix with some details of the momentum twistor formalism used to provide a rational parametrisation of the kinematics.
Computational setup and amplitude reduction We take a diagrammatic approach to the calculation of the amplitude along the lines of previous work  [81, 82]. Here we briefly summarise the steps and refer the reader to Ref. [82]  for details. All Feynman diagrams are generated using QGRAF  [83]  and subsequently processed using a combination of in-house Mathematica and FORM  [84, 85]  scripts. In total, including contributions from ghost diagrams, we find 50 diagrams at one loop and 1527 at two loops. Aided by the Spinney  [86]  package to perform the 't Hooft algebra, the numerators are written for each independent helicity configuration. From the loop denominator structure we assign an integral topology to each diagram. At this point, the diagram numerators are linear combinations of monomials in loop-momenta dependent scalar and spinor products with coefficients depending only on external momenta.
The code is available through https://bitbucket. org/njet/njet, where we provide additional installation instructions and example programs demonstrating the usage. The C++ code returns the values of the one-and two-loop hard functions, H (1) and H (2) , obtained by squaring Eq. (2. 8), substituting the decomposition in N c and n f from Eq. (2. 9), subtracting the IR and UV poles, and finally summing over colour and helicity, H = α 2 α 3 s (4π) 5 H (1) + α s 4π H (2) + O(α 5 s ) , H (2) = N c H (2) 1 + 1 N c H (2) 2 + n f H (2) 3. (6. 2) The sum over colours for each helicity can also be returned if required. We find the evaluation time is dominated by the special functions, particularly when higher precision is required. In order to ensure fast and stable numerical evaluation, we adopt the following evaluation strategy.
Foundation (SNF) under contract 200020-175595. HBH was partially supported by STFC consolidated HEP theory grant ST/T000694/1. DC is supported by the French National Research Agency in the framework of the "Investissements d'avenir" program (ANR-15-IDEX-02). RM was supported by STFC ST/S505365/1 and ST/P001246/1. SZ gratefully acknowledges the computing resources provided by the Max Planck Institute for Physics and by the Max Planck Computing & Data Facility.
Acknowledgments We are grateful to  Vasily Sotnikov  for useful discussions. This project received funding from the  European Union 's  Horizon 2020 research and innovation programmes New level of theoretical precision for LHC Run 2 and beyond  (grant agreement No  683211 ),  High precision multi-jet dynamics at the LHC  (grant agreement No  772009 ), and Novel structures in scattering amplitudes (grant agreement No  725110 ), and from the  Swiss National Science.
Transfer learning (TL) proposes to enhance machine learning performance on a problem, by reusing labeled data originally designed for a related problem. In particular, domain adaptation consists, for a specific task, in reusing training data developed for the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because those usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains. In this paper, we experiment with TL for the task of Relation Extraction (RE) from biomedical texts, using the TreeLSTM model. We empirically show the impact of TreeLSTM alone and with domain adaptation by obtaining better performances than the state of the art on two biomedical RE tasks and equal performances for two others, for which few annotated data are available.
Introduction A bottleneck problem for training deep learningbased architecture on text is the availability of large enough annotated training corpora. This is especially an issue in highly specialized domains such as those of biomedicine. TL approaches address this problem by leveraging existing labeled data originally designed for related tasks or domains  (Weiss et al. , 2016). However, adaptation between dissimilar domains may lead to negative transfer, i. e. transfer that decreases the performance for the target domain. In this article, we apply a TL strategy using the TreeLSTM model for the task of biomedical Relation Extraction (RE). We propose an analysis of the syntactic features of source and target domain corpora to provide elements of interpretation for the improvements we obtained.
, 2016). However, even if the source and target domain share the same language (i. e. , English), thus a common syntax, TL between domains may lead to negative transfer since specific source domains may use specific vocabularies as well as specific formulations that are inadequate to the target domain. Hence, we need to better understand and characterize what makes a source corpus potentially helpful, or harmful, with regard to a RE task. The contribution of this paper is twofold. First, we show that, compared to a baseline Convolutional Neural Network (CNN)-based model, a syntax-based model (i. e. , the TreeLSTM model) can better benefit from a TL strategy, even with very dissimilar additional source data. We conduct our experiments with two biomedical RE tasks and relatively small associated corpora, SNPPhenA  (Bokharaeian et al.
Hyperparameters were fixed to d w = 100, d e = 10, d h = 100 for each of the 2 channels, d s = 2 × d h = 200. We used two kernels of size 3 and 5 respectively. We applied a dropout regularization after the embedding layers  (Srivastava et al. , 2014)  with a dropout probability fixed to 0. 25. Table  2 : Results of our TL strategy in terms of precision (P), recall (R) and f-measure (F). σ F is the standard deviation of the f-measure. The + in the column Train corpus indicates that we trained our model using the target corpus plus one additional source corpus. TreeLSTM model. Dependency trees were derived from parsing trees obtained using the Charniak-Johnson parser trained on GENIA and PubMed data  (McClosky and Charniak, 2008). Hyper-parameters were fixed to d w = 100, d e = 10, d h = 200 and d s = 200.
Receiving Beacon Messages Protocol Vehicles are always ready to receive BSMs. When receiving a BSM, the receiving vehicle v i considers the sender's position and calculates the distance between itself and the sender. By doing this simple calculation, v i will be able to get a set of useful information that will determine its behavior. The pseudo-code of receiving a beacon message in WHISPER is illustrated in Algorithm 1. The main conclusions that v i is going to have after parsing the BSM sent by v j are the following:.
Transmission Range Adjustment Protocol Each vehicle v i , and after the Beacon_Interval_Time expires, will send a BSM to inform the nearby vehicles about its location. Particularly, WHISPER adjusts the transmission range prior to the final BSM broadcast. The adjustment is done each time a BSM is received by v i as explained before. When going to broadcast, v i uses the value of Speed to decide the appropriate transmission range (between all of the four levels: Low, Medium, beyond-Medium, and High). Algorithm 2 shows the pseudo-code of sending a BSM after making the transmission range adjustment step. Additionally, Speed is reinitialized to 0 after that and Checking_Pseudonym_Change_Trigger() is called during this protocol and that is to see the eligibility of changing v i 's pseudonym (and certificate respectively).
Performance Evaluation To validate the performances of WHISPER, we use simulation runs in a manhattan grid model created using the NETEDIT script included in SUMO; the mobility simulator  [43]. SUMO is considered as one of the most credible and realistic mobility simulators. The mobility and environment information used for the simulation are presented in Table  3. The manhattan grid model consists of 9 intersected roads with attached segments where each segment has a length of 200 m. Concerning the network simulator, we use OMNeT++  [44] ; the component is c++ based and discrete events simulator. OMNet++ allows the integration of diverse frame-works depending on the simulation nature like Veins  [45] , which is the vehicular network simulator. Veins acts as a bridge between the mobility simulator SUMO and the network simulator OMNet++.