dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2005) , measurements  (Lerner, 2019) ). Other software have recently started following this route as well, providing a suite of notebooks (based on FRETBursts(A  Ingargiola et al. , 2016)  and available on the smfBox  (Ambrose et al. , 2019)  GitHub, https://craggslab. github. io/smfBox/) for the workflow from raw photon-HDF5 files(Antonino  Ingargiola et al. , 2016)  through the determination of all correction factors, arriving at absolute, accurate FRET efficiencies. Although the notebook approach offers advantages to software developers and experienced users, it might be difficult for many end-users to navigate the command-line environment and adapt to the script-based workflow. For such users, it is safer and more convenient to use established and tested algorithms embedded in graphical user interfaces (GUIs).
Indeed, there is a large variety of user-friendly software available (Table  1 ). As a first step towards this goal, we propose that a software library (e. g. a Python package) should be established that contains established and tested algorithms for the analysis of fluorescence experiments, allowing them to be efficiently distributed and implemented in existing workflows. Such efforts have already been initiated in the FRETbursts software package(A Ingargiola et al. , 2016) and a GitHub group has been established at https://github. com/Fluorescence-Tools to collect software packages and connect software developers. http://ambermd. org/doc12/Amber20. pdf 6. 6 Guidelines and recommendations of using smFRET for integrative structural biology One important example for developing recommendations and standards for the community comes from the Worldwide Protein Data Bank (wwPDB)  (Berman, Henrick and Nakamura, 2003; Young et al.
In the long term, these recommendations could develop towards a standard for good scientific practice. The working principle of PDB-Dev relies on federating structural models and experimental data with appropriate data exchange, a proposal to establish a Fluorescence Biological Data Bank (FlBDB) for the archiving of data from fluorescence experiments is currently in progress. As a starting point, the PDB-Dev and the Seidel group have recently developed a dictionary for FRET data (https://github. com/ihmwg/FLR-dictionary) as a method-specific extension to the existing IHM dictionary so that FRET-restrained I/H structure models can now be deposited. To ensure that the reported results are reproducible, it is important that the raw data is sufficiently annotated. This includes reporting the sample details (e.
Acknowledgements We wish to thank  Niko Hildenbrandt  and  Sonja Schmid  for fruitful discussions. R. B. wishes to thank  Fabio D. Steffen  for insightful discussions. This position paper was supported by the  National Institutes of Health  ( NIH , grant  R01 GM130942  to S. W. , and to E. L. as a subaward; grant  R01 GM095904  to X. M. ; grants  R01 GM079238  &  R01 GM098859  to S. C. B. ), the  National Science Foundation (NSF , grants  MCB-1818147 & MCB-1842951  to S. W. ), the  Human Frontier Science Program  ( HFSP , grant  RGP0061/2019  to S. W. ), the  European Research Council  (ERC; grant numbers  638536 & 860954  to T. C. ; grant SMPFv2. 0 to E. A. L. ; grant No. 671208  (hybrid-FRET) to C. A. M. S. ), the  Deutsche Forschungsgemeinschaft (DFG , grants  GRK2062  ( C03 )  & SFB863  ( A13 ) to T.
C. ; grants  SPP2191 402723784 & SFB 1129 240245660  to E. A. L. ; grant  SE 1195/21-1 (SPP 2191 ) to C. A. M. S. ; Germany's Excellence Strategy ,CIBSS -EXC-2189 -project  ID   390939984  to  T. Hugel ), the  Wellcome Trust  (grant  110164/Z/15/Z  to A. K. ), the  Swiss National Science Foundation  (to B. S. ), the  UK Biotechnology and Biological Sciences Research Council  (grant  BB/S008896/1  to A. K. ; grant  BB/T008032/1  to T. D. C. ), the  Royal Society  (grant  RGS\R2\180405  to T. D. C. ), the  Agence Nationale de la Recherche  (grants  ANR-17-CE09-0026-02 ,  ANR-18-CE11-0004-02 & ANR-19-CE44-0009-02  to E. M. ), the  Israel Science Foundation  (ISF, grant  1250/19  to G. H. ), the  National Research Foundation of Korea  (grant  2019R1A2C2090896  to N. K. L. ), the  Independent Fund Denmark  (grant  6110-00623B  to V.
B. ), the  Milner Fund  (to E. L. ), the  Hebrew University of Jerusalem  (start-up funds to E. L. ), the  Ludwig Maximillian Universität (LMU , the  LMUinnovativ program BioImaging Network, BIN , to D. C. L. ), the  Biological Optical Microscopy Platform (BOMP) at University of Melbourne  (to H. S. ), the  University of Applied Sciences Mittweida  and the  Alfred-Werner-Legat of the Department of Chemistry, University Zurich  (financial support to R. B. ), the  EPSRC DTP Studentship  (to B. A. ) and the  CAS LMU fellowship  (to T. C. , S. W. , E. L. ). G. H. holds the Hilda Pomeraniec Memorial Professorial Chair. This work was performed under the auspices of the  U. S. Department of Energy  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344  (for T. L. ).
I. INTRODUCTION The number of people living in megacities, that is cities with population greater than 10 million, has increased from 69. 5 million in 1975 to a staggering 472. 8 million in 2015  [1]. The percentage of network traffic originating and terminating in a city is increasing  [2] , through the use of popular on-line services and smart city applications. It is expected that Smart Mega Cities will become the main source of data, characterized by massive data growth and processing requirements. Today these requirements are served by a variety of optical and wireless networking and edge/fog/cloud computing technologies and infrastructures deployed in cities and belonging to different providers, while smaller business owners (e. g. stadium operators) are also deploying their own infrastructures  [3].
The MARSAL project will contribute with many cell-free related innovations to the ORAN architecture. Specifically, distributed cell-free networking support will be implemented as part of the O-DU module, appropriately modifying the PHY, MAC, and RLC sublayers. The role of orchestration in the midhaul (NFVO, MEC) will also be considered. Moreover, cell-free Radio Resource Management (RRM) will be implemented as part of the Near-RT RIC, which will be derived from ACC's dRAX platform. The dRAX plug-in framework will be leveraged to implement the data-driven RRM functionality for the cell-free architecture in line with the O-RAN specifications for training and deploying ML inference within the Near-RT RIC  [12]. Extensions of existing interfaces and control plane protocols will be applied at various reference points (e.
IV. MARSAL'S VIRTUAL ELASTIC INFRASTRUCTURE PILLAR MARSAL envisages a new paradigm of Edge infrastructures based on the notion of Elastic Edge Computing, aiming For the control plane of the mobile segment, MARSAL proposes the disaggregation of the Non-RT into Near-RT SDN Control function that will be hosted by the Near-RT RIC at the Regional Edge nodes. Thus, near realtime reaction to workload variations will be supported, at subsecond timescales. Moreover, MARSAL proposes the deployment of Software-Defined Transport Network Controllers (i. e. , SDTNs) at the Regional Edge to control the fixed segment. Both domains will be federated under MARSAL's Core Tier NFVO, based on ETSI OSM, which will provide Network Slicing As a Service (NSaaS) functionality as per 3GPP TR 28. 801 specifications. Thus, end-to-end slicing with centralized orchestration is supported, while still allowing innovative closed-loop (or ML-driven) control of each individual domain.
MARSAL also proposes a novel Fixed Mobile Convergence (FMC) solution, to facilitate integrated connectivity of mobile and fixed (i. e. , FTTH) services. MARSAL's solution involves two transmission approaches seamlessly integrated at the Regional Edge node, including a standard Point-to-Point (PtP) connection with or without WDM and a very disruptive point-to-multipoint (PtMP) approach based on XGS-PON modules, as detailed in Fig. 5. An energy efficiency scenario will also be considered, with traffic aggregation on a limited set of wavelengths which will allow shutting-down individual SFP+ transceivers. Moreover, predictive dynamic slicing approaches will also be explored, leveraging an MLdriven control loop to trigger slice reconfiguration proactively, based on convolutional neural networks that predict traffic fluctuations.
While AONT has been previously studied in the context of P2P networks (  [15] ), it is proposed in MARSAL as an innovative solution in a multitenant infrastructure environment with potentially untrusted nodes, ensuring that the unavailability of a single data chunk prevents the reconstruction of any part of the original plaintext. MARSAL will also propose and implement an innovative NFS Gateway, controlled by the OTT application provider, to serve as the foundation of trust. The DCS gateway will be the intermediary between the (trusted) OTT application, and (untrusted) DCS infrastructure. The NFS gateway will be extended to implement a novel data pipeline for the controlled sharing of data among different parties. The gateway will support for first time a policy language, extending SoTA solutions such as Open Digital Rights Language or JSON-LD, effectively enforceable in the data protection context of different stakeholders that will permit the specification of sharing and processing restrictions over data.
C. Hardware Accelerated, ML-based data plane security and malicious traffic detection To improve the performance of current signature-based solutions for dealing with zero-day or evolving attacks, MARSAL provide hardware accelerated solutions for a decentralized Threat Detection Engine and a centralized Threat Analysis Engine. ML-based threat detection, that has demonstrated an improved ability to extract complex non-linear relationships in attack data , will be leveraged for the design of MARSAL's Threat Detection Engine (TDE). Moreover, MARSAL will leverage the capabilities of a new generation of MELX programmable SDN Switches, that support the new P4 programming language for stateful processing, header inspection, and packet metadata extraction, operating at wire speeds. The programmable pipeline of the aforementioned SDN Switches will be exploited in combination with the P4 stateful registers to perform cyberattack detection and mitigation via embedded ML algorithms (e.
As the results of development, each analysis code has the following differences. • Japan: The analysis code REVIAN-3D has developed to analyse and evaluate the seismic force in the vertical direction for the severe seismic condition in Japan. This code saves the computational resources by minimizing the degree of freedom of the model using a modal analysis method. In addition, it enables 3D analysis for whole core including vertical behavior with conserved the computational resources  (Fujita, 1981; Iwasaki, 2016 Iwasaki, , 2017a Iwasaki, ,b, 2018;; Kitamura et al. , 1999; Matsubara, 2017; Yamamoto, 2017 Yamamoto, , 2018 ). • France: The analysis code BASLIQ has developed using a generalpurpose FEM CAST3M. This code made the model three-dimensional as the computational resources increased.
French analysis method Physical and numerical methods and tools (CAST3M, xxxx) have been developed by CEA to describe and simulate the dynamic behavior of Fast Reactor Cores. These methods are integrated into the simulation tool BASILIQCAST3M, based on the finite element code CAST3M  (Broc, 2014). At this stage, only 2D analyses are supported by BASI-LIQCAST3M, 3D analyses are under development  (Broc, 2018). The S/As are represented by finite element beams, with a nonlinear model at the bottom. The insertion of the spike in the grid plate is modelled by an elasto-plastic behavior with kinematic hardening. This model of the equivalent joint ball of the spike provides a direct relationship between the rotation and the mechanical moment and takes into account a gap. The spike is not explicitly modelled.
Digital lock-in The functionality of iTPD was implemented in a Matlab (MATLAB 2017) software program to perform a digital lock-in on simulated and experimental data in retrospect. One fundamental difference between triggered and lock-in measurements is their operation with continuous data streams. Therefore, individual IA signals from triggered acquisitions or simulations were extended to multiples of the pulse repetition frequency of the accelerator into a single continuous signal. The total duration of the synthetic signals was 1 s which corresponds to 1000 signals considering a realistic 1 kHz pulse repetition frequency. First, a digital iTPD was carried out on an IA simulation (see section 2. 4. 2) considering 138 MeV protons stopping in an artificial heterogeneous geometry. Noise was added to the simulated pressure trace mimicking a similar SNR of -15 dB to  Lehrack et al (2017).
Experimental data at 220 MeV The results of a digital lock-in on measured data are shown in figure  10. Firstly, figure  10(a)  shows the experimental data from  Lehrack et al (2017). The blue line presents one triggered IA measurement acquired with the hydrophone. The orange line depicts the averaged and inverted prompt gamma ray signal measured with a fast plastic scintillator (Bicron BC400 R, 4. 5 cm) lateral to the beam axis. The black line shows the 1000-fold averaged IA signal where the Bragg peak signal near 300 μs can be separated from noise. The scintillator and IA signal were input into a digital lock-in amplifier to derive the ToF via iTPD. The vertical black line in figure 10(c) presents the reference result ToF τ extracted from averaged signals in 10(a) according to  Lehrack et al (2017).
Acknowledgments This work was funded by the  German Research Foundation (DFG)  project  403 225 886 ,  24 819 222  and the  European Research Council  grant  725 539. The author kindly acknowledge support of  Jakob Kainhuber ,  Ronaldo Kalunga ,  Pratik Dash K. ,  Benjamin Wollant ,  Katrin Schnürle  and operators of the Tandem-Van de Graeff accelerator in Garching and  Claudius Riek  from  Zurich Instruments.
Learning from Multivariate Time Series (MTS) has attracted widespread attention in recent years. In particular, label shortage is a real challenge for the classification task on MTS, considering its complex dimensional and sequential data structure. Unlike self-training and positive unlabeled learning that rely on distance-based classifiers, in this paper, we propose SMATE, a novel semi-supervised model for learning the interpretable Spatio-Temporal representation from weakly labeled MTS. We validate empirically the learned representation on 30 public datasets from the UEA MTS archive. We compare it with 13 state-of-the-art baseline methods for fully supervised tasks and four baselines for semi-supervised tasks. The results show the reliability and efficiency of our proposed method.
I. INTRODUCTION Most Multivariate Time Series (MTS) data, such as sensor readings, are labeled during the data collection process. The post-labeling of MTS is much more costly than classic data (e. g. , images, text, etc. ) due to the low interpretability over the real-valued sequence, leading to a considerable constraint for MTS classification in real-life scenarios. Weakly supervised learning becomes an alternative option to fully supervised algorithms. The previous studies on weaklabel Time Series (TS) learning are usually based on selftraining  [1]  or Positive Unlabeled Learning  [2]  with a carefully designed distance measure  [3]  or stopping criterion  [4]  to learn the pseudo-labels. Besides, they mostly focus on the Univariate Time Series (UTS) with the One-Nearest-Neighbor (1NN) classifier on raw data space, which is widely outpaced by today's advanced approaches  [5] , such as Deep Neural Networks (DNNs)  [6]  or ensemble methods  [5].
This regularization process comes with the capability of visualization at each step, making SMATE interpretable. We summarize the paper's main contributions as follows: • Spatio-temporal dynamic features in MTS: We claim and demonstrate that the temporal dependency and the evolution of the spatial interactions (spatial dynamics) are important for building a reliable MTS embedding. The experiments are carried out on 30 MTS datasets from different application domains. The detailed evaluation with 13 supervised baselines and four semi-supervised work is provided, which shows the effectiveness and the efficiency of SMATE over the state of the art. The rest of this paper starts with a review of the most related work. Then, we formulate the problems of the paper. Later, we present in detail our proposal SMATE, which is followed by the experiments on real-life datasets and the conclusion.
MC-DCNN  [13]  extracts firstly 1D-CNN features from each variable, then combines them with a Fully Connected (FC) Layer. Whereas the authors in  [19]  abandon the combination strategy but apply directly 1D-CNN to all variables. The 2D-CNN features with the crossattention mechanism in CA-SFCN  [6]  enhanced the dependencies captured by 1D-CNN on both temporal and spatial axes. Last but not least, the hybrid LSTM-CNN structure is capable of extracting both local and long-term features. Various work, such as the Squeeze-and-Excitation block in MLSTM-FCN  [12]  or the multi-view learning-like module in TapNet  [20] , enhanced the hybrid structure via modeling the spatial interactions. However, the interactions are generally computed at the sequence level, ignoring the fact that the local spatial interactions at the sub-sequence level may evolve in the dynamic sequence, i.
B. Semi-supervised Learning on Time Series The pioneering work  [1] ,  [3]  on Semi-supervised TS Learning are based on self-training or Positive Unlabeled Learning  [2]  with the Nearest-Neighbor (1NN) classifier and a carefully designed distance, such as DTW  [1]  or DTW-D  [3] , and optimized stopping criterion  [4]  for importing the pseudolabels. Those work are followed by  [21]  for wider contexts. Though not mentioned in their papers, the self-training framework is extensible from the UTS to the MTS setting by using an adapted distance  [22] , such as DTW D or DTW A. However, under more complex scenarios nowadays, such as 30 UEA MTS datasets  [23]  collected from different domains, the distance-based classifiers show limited performance  [5]. Learning MTS representations  [14]  in a weakly supervised setting draws much attention recently.
B. Semi-Supervised Learning on MTS Definition 2 (weak-label MTS dataset): A weakly labeled MTS dataset D = {D l , D u } contains both labeled and unlabeled MTS samples: D l = {xi, yi} N * r i=1 , Du = {xi} N * (1-r) i=1 r (0 ≤ r ≤ 1) indicates the ratio of the labeled samples in D of size N, y i is the annotation of the labeled instance x i. The semi-supervised MTS learning aims at training a classifier to predict successfully the label of a testing MTS sample, adopting the supervised training from D l and further unsupervised adjustment/optimization from D u.
V. EXPERIMENTS A. Experimental setup The model 2  was trained using the Adam optimizer on a single Tesla V100 GPU of 32 Go memory with CUDA 10. 2. We train an SVM classifier with radial basis function kernel on the learned embedding space, which is evaluated on the newly released UEA archive  [23]   3  for supervised analysis, where the datasets {ArticularyWordR. , Epilepsy, Heartbeat, SelfRegulationSCP1} from four different domains are adopted for semi-supervised study.
66). Besides, USRL and SMATE NR perform much worse than SMATE with the same SVM classifier, confirming the reliability of our supervised regularization on the embedding space. Moreover, SMATE achieves the best performance among the baselines on all the datasets of EEG/MEG applications  [23]  (FaceDetection, FingerMovements, Hand-MovementDirection, MotorImagery, SelfRegulationSCP1, Sel-fRegulationSCP2), where the signals (i. e. , variables) generally have strong and dynamic dependencies with each other. The spatial dynamic interactions could be essential characteristics that SMATE has successfully captured.
D. Visualization & Interpretation of the Representation Space Apart from the thorough exploration of the weakly labeled samples, the representation space learned via SMATE shows good interpretability compared to the traditional Deep Learning models  [6] ,  [12] ,  [13] ,  [19]. We show in Figure  4  that t-SNE visualization of the representation space for the Epilepsy dataset, which contains four human activities: Walking while gesturing, Walking slowly, Walking fast, Walking normally. The 137 samples in the training set are projected to the representation space with 10% labels adopted for training. We show respectively the space visualization at each regularization step. The results suggest that the representation space is interpretable for not only the effect of the weak supervision at each step but also the classification results.
E. Performance of Spatial Modeling Block (SMB) To validate the Spatial Modeling Block (SMB), firstly, we compare the classification accuracy of SMATE with or without integrating SMB on the 27 datasets that SMATE has successfully executed. Then we rebuild SMATE by replacing SMB with the following modules in the state-of-the-art work which learn the variable relationships of MTS: Random Dimension Permutation (RDP) in TapNet  [20]  and Squeez-and-Excitation (SE) in MLSTM-FCN  [12]. Briefly, SMATE-SMB achieves  [17 Wins|8  Ties|2 Losses] to SMATE-NonSMB, indicating that SMB contributes to a better MTS representation. Besides, SMB performs better than other modules:  [14 Wins|8  Ties|5 Losses] to SE,  [12 Wins|9  Ties|6 Losses] to RDP. RDP performs relatively better than SE, as a set of grouped variables produced by RDP provides various MTS views, allowing exploring the interactions between the subsets of all variables more thoroughly.
ACKNOWLEDGEMENTS This research was supported by DATAIA convergence institute as part of the  Programme d'Investissement d'Avenir  ( ANR-17-CONV-0003 ) operated by  DAVID Lab, UVSQ ,  Université Paris-Saclay. The authors would like to thank  Anthony Bagnall  and his team for providing the community with valuable datasets and source codes in the  UEA & UCR Time Series Classification Repository. The authors would also like to thank  Eamonn Keogh  for his careful review and remarks.
Statistical modeling and machine learning can be used in several applications, such as motion prediction, increasing the realism of simulations, etc  [8]  In recent years, many techniques have been developed to process this large volume generated by fluid mechanics, ranging from advanced algorithms for processing and compressing or merging data to fluid mechanics databases. However, the analysis of fluid mechanics data has relied, to a large extent, on domain expertise, statistical analysis and heuristic algorithms  [9]  First principles, such as conservation laws, have been the main building blocks of flow modeling in recent years. However, for high Reynolds numbers, scale-resolution simulations using the most important model in fluid mechanics, the Navier -Stokes equations, are beyond our current computational resources.
A. STRIP THEORY AND WAKE OSCILLATOR MODELS: CONTEXT This section returns to the application of strip theory for overhead line operators. Some results and foundations in the use of this modeling of fluid and structures interaction are established there. However, given the numerical implementation cost associated with such simulations, a simplification of this interaction is proposed via a wake oscillator model. This part reviews the work required to use the strip theory database to calibrate the wake oscillator and its theoretical basis. A database of Fluid Structure Interaction (FSI) results is necessary to calibrate and validate simplified modelling. A FSI architecture has been developed previously  [3] , which includes a vibrating string model to represent the cable coupled to a series of Computational Fluid Dynamics (CFD) 'strips'.