dataesr/scientific-paragraphs-categorization huggingface dataset:
The small difference from the simulated transmission performed on an AWGN communication channel is due to noise introduced by the RTL-SDR device and wireless transmission impairments. The proposed drivers' warning application has the following clear advantages compared to other similar ones: (1) increase drivers' safety by warning them of imminent dangers that may occur in traffic: traffic jams, wet road, works on public roads, rugged roads, impassable roads, car accidents, worsening weather, bypassing certain obstacles in traffic, landslides, police control; (2) the proposed application is inexpensive and easy to implement because it uses an existing and very widespread radio FM infrastructure; the proposed system includes an existing FM broadcasting station -for transmission and a cheap RTL -SDR device, attached to a Tablet PC (present in most of the cars today) and connected to the car aerial -for receiving; this significantly lowers the cost price of the system; (3) the FM radio ensures a very good quality of the received signal and good coverage of a large geographical area; (4) the application provides reliable data transmission at constant rate even in noisy conditions; (5) the application allows simultaneous reception in real-time of the image notifications together with the usual FM radio broadcasting;  (6)  image notifications are easy to receive and understand by drivers without distracting them from driving and do not require the interruption of the radio station broadcasting;  (7)  signaling dangerous situations in traffic is made by the road authorities and not by other traffic participants, as in the case of other applications; (8) the proposed application does not require an Internet connection, which may be poor in certain geographical areas along public roads; (9) the proposed system is flexible, easily adapts to the transmission of any type of image notification, and can be easily installed and used by drivers;  (10)  the proposed application can also be used in cars that do not have a Tablet PC, by sending the warning notification in a simplified form, as a text message on the RDS signal of the FM broadcasting.
UV melting UV melting profiles were recorded on a Uvikon XL UV/Vis spectrophotometer coupled to a Peltier thermosystem (Biotek) and an automated cell changer for 10 cells and 2 references. The temperature controller was ramped at a rate of 0. 5°C / min from 10°C to 90°C, and data points were collected every 0. 4°C as determined by a temperature probe inserted into a cuvette containing the final buffer. Samples were measured in 10 mM sodium cacodylate buffer pH 7, 0. 5 mM EDTA and 100 mM NaCl at an RNA concentration of 25 nM. For data analysis, a script was written to convert concatenated Uvikon report text files into separate denaturation and annealing segments for each position. These were subsequently subjected to piece-wise linear interpolation and smoothed over a 5°C window. From this data, the first derivative of absorbance curves (dA 260 /dT versus T) were calculated and plotted as a function of temperature, using either Kaleidagraph 4.
In vitro reconstitution of RNase P holoenzymes was performed as follows: P RNAs were incubated in buffer KN for 5 min at 55°C and 50 min at 37°C, after which RNase P protein was added, followed by another 5 min at 37°C before addition of substrate. Cleavage assays were performed at 37°C. Aliquots of the cleavage reactions were withdrawn at various time points and analyzed by electrophoresis on 20% polyacrylamide/8 M urea gels. Data analysis and calculations were performed essentially as previously described  (Busch et al. 2000). Briefly, gels were subjected to phosphorimaging and radioactive bands representing substrate and 5'-cleavage product were quantified with the AIDA (raytest) image analysis software. First-order rate constants of cleavage (k obs ) were calculated by fitting the data to the equation for a single exponential: f cleaved = f endpoint × (1 -.
Cold Spring Harbor Laboratory Press on August 26, 2021 -Published by rnajournal. cshlp. org Downloaded from e -(kobs) × t ), where f cleaved = fraction of substrate cleaved, t = time, f endpoint = maximum cleavable substrate (Grafit version 5. 0. 13, Erithacus Software). For determination of single turnover V max (k react ) and K M (K M(sto) ) values, k obs values measured at 5 different enzyme concentrations (based on at least 3 replicate experiments each) were plotted against the enzyme concentration; k react and K M(sto) were obtained by fitting the data to a "Michaelis-Menten-like" enzyme kinetics model: k obs = k react × [P RNA]/(K M(sto) + [P RNA]). For experimental conditions in Supplemental Fig. S4, see figure legend.
LocKedge is implemented in two manners: centralized and federated learning manners in order to verify the performance of the architecture from different perspectives. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT-IoT data set. The results show that LocKedge outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.
At the Gateway, we develop an accurate lightweight multi-attack detection module called LocKedge that can detect different types of at-tacks. When an attack is detected, the Gateway traces its source then blocks the malicious connections. The Gateway can either send its processed data to the cloud for data mining purposes in the centralized mode (i. e. centralized learning), or train the detection module locally and then sends the weights of the model to the cloud for aggregation in the federated mode (federated learning). In case of emergency when a particular attack is too intense or its source cannot be determined in time, the Gateway can simply block all incoming data from its zone, not affecting the cloud or any other legitimate sources of other zones. Detecting and mitigating attacks right at each zone will make the system response faster and more effectively since: (1) it is near the attack sources so detection time is smaller; (  2 ) it has to deal with a smaller set of data from one zone only and thus lessen the processing time and computing capacity requirement; and (3) in the worst case scenario, only the affected zone is down, the cloud is still protected and the damage is minimized.
• Network layer: The network layer which secures data transfer from the lower layer to the higher layer, so it plays an important role in a general architecture. • Data management layer: The cloud. Within the scope of security, the cloud is designed for analyzing given IoTdevice data sets. The Optimization Module developed in the Cloud is responsible for analyzing data and deciding the number of neurons per layer as well as the weights of the neural network algorithm. Periodically, the cloud sends the aforementioned information to all gateways. Hence, the cloud deals with big data and the computing phase, and its new rules will be updated to all IoT gateways for more efficient protection of the network. • Application layer: is responsible for inclusive applications management based on the processed information in the Data management layer such as intelligent transportation, smart car, mart health, identity authentication, smart glasses, location, and safety, etc.
It helps the system to reduce complexity for computing full features of a dataset. • Centralized learning in the cloud to define the number of neurons per hidden layer and the number of layers in order to receive high accuracy and low complexity of the detection phase. • Design and evaluate the centralized and federatedlearning based detection solutions to cope with facts of IoT networks In the following section, we will present the detection solutions in both ways aforementioned. We will also elaborate the reasons to use each of the solutions and perform an evaluation for the two methods.
A. FEATURE EXTRACTION AND DEMENSION REDUCTION MODULE There are many techniques and corresponding algorithms that can reduce the dimensions of data, the authors in  [19]  have divided them into 4 groups: Feature Ranker, Feature Evaluator, Dimensionality Reduction and Clustering Algorithms. In this research we experimented with some different algorithms with the BoT-IoT dataset  [18]  to evaluate the performance. Finally, we choose the Principal Components Analysis (PCA) method  [17]  to extract the most important features due to its better performance and since PCA is fast and computationally cheap. With PCA, the original data points will be transformed into a new space, where it is possible to differentiate the importance of the components together. The size of data dimensions is decreased from d to k which are k important components of the data in the new space system.
B. MULTI-ATTACK DETECTION MODULE In this module, we deploy a Neural Network (NN) to detect multiple types of attacks as shown in Figure  4. Optimizing the number of layers and the number of neurons per layer is directly related to the algorithm complexity. Therefore, in this study, we try to optimize the number of layers and neurons per layer for the BoT-IoT data set to balance complexity and accuracy performance for the multi-attack classification problem. Fig. 4  illustrates the main components of a NN. The function of NN is to perform complex mapping and convert input information into outputs, which is defined mathematically as F:   →. Accordingly, the input information  passes through each layer and is transformed by Error! Reference source not found. until it reaches the output layer with the softmax activation function for multi-class problem, which is denoted as  ̂= (.
V. PERFORMANCE EVALUATION In this research we use the BoT-IoT dataset  [18]  to evaluate our model. This data set was generated by designing a realistic IoT network environment, with five IoT scenarios: a weather station, a smart fridge, a remotely activated, motion activated lights and a smart thermostat  [23]. We used version 5% extracted from original dataset proposed in  [23]. It includes 10 types of attacks: DDoS (HTTP, TCP, UDP), DoS (HTTP, TCP, UDP), OS, Service Scan, Keylogging and Data exfiltration attacks. The number of each type is shown in.
B. DETECTION PERFORMANCE OF THE CENTRALIZED-LEARNING LOCKEDGE Firstly, In the centralized scenario, all the data is gathered at the cloud server for training and testing of the model. Thus, for this evaluation, the dataset was used in one piece, without being divided into smaller datasets, as is the case in the next section. Firstly, we compared the performance of the centralized LocKedge with the pure Neural Network Model (NN) without the feature processing in terms of Accuracy, Detection rate and Complexity when performing a multi -attack classification. The number of neurons in the hidden layer h in our experiment ranges from 6 to 46. The results are shown in Fig. 7 , where: • Accuracy is the total number of correctly predicted samples in all tests. • Detection rate (DR) is the number of the actual positives that are predicted as positive In Fig.
There are four attacking sources in the BoT-IoT testbed, with their IP addresses ranging from 192. 168. 100. 147 to 192. 168. 100. 150, so we assume that each source attacks a different gateway. All other source IP addresses are treated as normal or victim devices in one of the four zones. Each dataset is then divided into a training and testing set, so we will have 4 train sets and 4 test sets. The feature extraction phase (PCA) is performed using all 4 training sets together, then the detection model will be trained by the training sets separately using the federated learning approach. After each communication round, the resulting global model will be evaluated using the 4 different test sets. Fig. 11and Fig. 12show the accuracy and the loss after 1000 communication rounds of the test. The number of local epochs was set to 1, after empirical testing showed that this greatly reduces the training time.
Results Parameterization of an abj-DEB model for Xylonora atlantica. For the parameter estimation, the completeness of real data  31  within the abj-DEB model developed for X. atlantica was 2. 6 using the dataset of online Supplementary Table  S2. This value of 2. 6 is in the mean of the values found in the ' Add_my_pet' database, highlighting the good quality and availability of the data. The goodness of fit  31  resulted in a mean relative error (MRE) of 0. 148 and a symmetric mean squared error (SMSE) of 0. 141, demonstrating a good match between observations and predictions. The model has been validated and published online within the ' Add_ my_pet' database (https:// www. bio. vu. nl/ thb/ deb/ deblab/ add_ my_ pet/ entri es_ web/ Xylon ora_ atlan tica/ Xylon ora_ atlan tica_ res. html).
Discussion The first goal of the study was to evaluate whether the primary and core parameter values of the abj-DEB model of Xylonora atlantica were in line with those of shallow marine bivalves and more specifically with those of a shallow wood borer, such as Teredo navalis, for which the abj-DEB models were developed (AMP database  32  ). A number of DEB parameters were in the same range, but some of them were very different and could highlight some specific adaptations to the deep-sea habitat or to the wood-fall habitat. For instance, the maximum assimilation rate ṗAm of X. atlantica was twofold lower than those of shallower marine bivalve species but ten-to 15-fold higher than that of the symbiotic shallow marine bivalve Thyasira cf. gouldi that relies on food both from its sulfur-oxidizing bacteria and POM  33.
flexuosa or T. cf. gouldi, AMP database). This result reveals a lower mobilization capacity of reserve E in wood borers, enhancing a greater maximum reserve capacity ([E m ]) as they are inversely proportional. Due to these higher [E m ] values, in comparison to other marine bivalves, wood-boring bivalves may have higher survival rates in starvation periods, highlighting some specific adaptation to wood-fall habitats, which are usually fragmented and ephemeral in the ocean. This scenario would explain why some deepsea xylophagaid species have been shown to survive in habitats other than wood (cable, plastic, vinyl, etc. ) using these latter items only as shelters  30. The volume-specific somatic maintenance ṗM and the somatic maintenance rate coefficient ( kM ) of X. atlantica that play a role in the turnover of the structure (DNA, enzymes, etc.
Synthesis of data for Xylonora atlantica and DEB parameter value estimation. For all zerovariate data, the value of the scaled functional response f was set to 1, considering that only the 'best individual' was used. Temperature was taken into account in the zerovariate dataset for age and in all the univariate dataset with rates in order to apply a temperature correction (Supplementary Material S1 online). A number of early-stage zerovariate data (Supplementary Table  S2  online) were obtained from a larval culture  21  where some mature female X. atlantica released spawned eggs spontaneously in the laboratory, revealing internal fertilization. Larvae were reared until the pediveliger stages. Several key data were used, such as age and length at hatching (trochophore), birth (umboveliger) and metamorphosis (pediveliger).
For puberty (occurrence of sexual maturity) and fecundity, a histological study  24  was used to provide shell height (SH) and wet weight of the youngest mature female X. atlantica and the maximum reproduction rate. The age at puberty was deduced from a colonization experiment  22  carried out at 100 m in the North Atlantic Ocean giving shell height following time and the size at first reproduction defined by the previous histological study  24. Only one study  8  has reported the maximum shell height and lifespan observed in X. atlantica. The univariate dataset (Supplementary Table  S2  online) was retrieved both from the literature and from the authors. First, two in situ colonization experiments  22  at 100 m in depth followed the shell height (SH) of Xylonora atlantica recruits for almost one year at the edge of the continental shelf, south of Cape Cod (North Atlantic Ocean), using two different types of substrates (pine and oak).
Second, specimens (333 juveniles/adults X. atlantica) from a colonization experiment (CHEMECOLI)  23, 24  carried out at 2300 m in depth in the mid-Atlantic ridge for one year and fixed in FISH fixative were used. The shell height (SH) of each individual was measured using a BA210 Motic digital compound microscope with Motic Image 3. 0 software and weighed (wet weight) to calculate the shape (δ M ) for juveniles/adults. The scaled functional response f was set to 1 for the pine colonization experiments, whereas the other univariate dataset remained free, as for the oak colonization experiments. Parameter estimation  32  of the DEB model was carried out using the Xylonora atlantica dataset of online Supplementary Table  S2. The estimation of the parameters was possible using the package DEBtool, which is updated periodically (https:// github.
Comparison of primary and core parameters of the abj-DEB model of the Atlantic woodeater Xylonora atlantica with those of Teredo navalis at T ref = 293 K (https:// www. bio. vu. nl/ thb/ deb/ deblab/ add_ my_ pet/ entri es_ web/ Teredo_ naval is/ Teredo_ naval is_ res. html) and the mean values of 29 marine bivalve species that are found in the AMP database (https:// www. bio. vu. nl/ thb/ deb/ deblab/ add_ my_ pet/ speci es_ list. html), where abj-DEB models were used. SD standard deviation.
Acknowledgements Authors would like to thank the people who have deployed and recovered the CHEMECOLIs:  Delphine Cottin  and  Amandine Nunes Jorge. We are grateful to chief scientists, captains and crews of  RVs Pourquoi  pas? and  L'Atalante , and teams operating ROVs Victor 6000 (Ifremer, France) and submersible Nautile (Ifremer, France) during the cruises  MOMARDREAM-07  and  MOMARDEAM-08. Sample collection and construction of the CHEMECOLIs were funded by  CHEMECO (European Sciences Foundation  ( ESF)/Eurocores/EURO-DEEP/0001/2007 ). We would like to thank  Sven Laming  that sorted some wood-boring bivalves in laboratory and Théo Lancelot that measured and weighted them. We are grateful to the people from the  DEB  school at Brest in 2019 that permits to initiate this research on DEB theory with fruitful discussion.
Data availability The abj-DEB model of Xylonora atlantica is accessible on the database of Add-My-Pet (https:// www. bio. vu. nl/ thb/ deb/ deblab/ add_ my_ pet/ entri es_ web/ Xylon ora_ atlan tica/ Xylon ora_ atlan tica_ res. html) and provides the results output of the model. Several scripts in Matlab were used to infer the functional traits of X. atlantica and can be requested to SMG.
A high-performance implementation of a multiphase lattice Boltzmann method based on the conservative Allen-Cahn model supporting high-density ratios and high Reynolds numbers is presented. Metaprogramming techniques are used to generate optimized code for CPUs and GPUs automatically. The coupled model is specified in a high-level symbolic description and optimized through automatic transformations. The memory footprint of the resulting algorithm is reduced through the fusion of compute kernels. A roofline analysis demonstrates the excellent efficiency of the generated code on a single GPU. The resulting single GPU code has been integrated into the multiphysics framework WALBERLA to run massively parallel simulations on large domains. Communication hiding and GPUDirect-enabled MPI yield near-perfect scaling behaviour.
Thus we may need simulation domains containing billions of grid points and beyond. This creates the need for using high-performance computing (HPC) systems and for a highly parallelized and run-time efficient algorithms. However, highly-optimized implementations in low-level languages like Fortran or C often suffer from poor flexibility and poor extensibility so that algorithmic changes and the development of new models may get tedious. To overcome this problem, we here use a work flow where we generate the compute intensive kernels at compile time with a code generator realized in Python. Thus we obtain the highest possible performance while maintaining maximal flexibility (see  Bauer et al. , 2019). Furthermore, the symbolic description of the complete ACM allows us to use IPython (see  Pérez and Granger, 2007)  as an interactive prototyping environment.
The remainder of the article is structured as follows. In section 2, we will summarize related work to the conservative ACM. In section 3, we will introduce the governing equations of the conservative ACM. Section 4 presents details of the implementation, by first introducing the code generation toolkit lbmpy by  Bauer et al. (2020b)  which constitutes the basis of our implementation. Then, we present the phase-field algorithm itself in a straightforward and an improved form, where the improvements essentially lie in the minimization of the memory footprint. This is primarily achieved by changing the structure of the algorithm in order to be able to fuse several compute kernels. The performance of our implementation is discussed in section 5. We first show a comparison of the straightforward and the improved algorithm.
Code Generation Our implementation of the conservative ACM is based on the open source LBM code generation framework lbmpy * (see  Bauer et al. , 2020b). Using this meta-programming approach, we address the often encountered trade-off between code flexiblity, readability, and maintainability on the one hand, and platform-specific performance engineering on the other hand. Especially when targeting modern heterogeneous HPC architectures, a highly optimized compute kernel, may require that loops are unrolled, common subexpressions extracted, and possibly hardware-specific intrinsics are used. In state-of-the art optimized software (see  Hager and Wellein, 2010) , these transformations are essential, and must be performed manually for each target architecture. Clearly, the resulting codes are time-consuming to develop, are error prone, hard to read, difficult maintain and often very hard to adapt and extend.
Flexibility and maintainability have been sacrificed, since such complex programming techniques are essential to get the full performance available on the system. Here, in contrast, we employ the LBM code generation framework lbmpy. Thanks to the automated code transformations, the LB scheme can be specified in a high-level symbolic representation. The hardware-and problem-specific transformations are applied automatically so that starting form an abstract representation, highly efficient C code for CPUs or CUDA/OpenCL code for GPUs can be generated with little effort. Our new tool lbmpy is realized as a Python package that in turn is built by using the stencil code generation and transformataion framework pystencils † (see  Bauer et al. , 2019). The flexibility of lbmpy results from the fully symbolic representation of collision operators and compute kernels, utilizing the computer algebra system SymPy(see  Meurer et al.
, 2017). The package offers an interactive environment for method prototyping and development on a single workstation, similar to what FEniCS (see  Alnaes et al. , 2015)  is in the context of finite element methods. Generated kernels can then be easily integrated into the HPC framework WALBERLA, which is designed to run massively parallel simulations for a wide range of scientific applications (see  Bauer et al. , 2020a). In this workflow, lbmpy is employed for generating optimized compute and communication kernels, whereas WALBERLA provides the software structure to use these kernels in large scale scenarios on supercomputers. 'lbmpy can generate kernels for moment-based LB schemes, namely single-relaxation-time (SRT), two-relaxation-times (TRT), and multiple-relaxation-time (MRT) methods.