dataesr/scientific-paragraphs-categorization huggingface dataset:
Adjusted p-values are calculated, correcting for multiple comparisons using the false discovery rate (FDR) method. A complementary analysis used an existing method  (Martinez Viedma & Pickett, 2018)  to retrieve all genes within the significantly affected Reactome signaling pathways  (Jassal et al. , 2020). The UniProt identifiers for these genes were then determined prior to searching the opentargets. org database for small molecules or biologics that bind to one or more genes in the significant pathways.
Project 5: Reproducible workflows With the goal of making our protocols available to the scientific community, we have packaged the software and pipeline described in Project 1 Pipeline 1. The fully reproducible, containerized pipeline was implemented in Nextflow  (Di Tommaso et al. , 2017)  based on the nf-core framework  (Ewels et al. , 2020)  to enable the reuse for additional data sets and verification generated results. Figure  7  depicts the proposed reproducible workflow. The pipeline for this workflow can be found at https://github. com/chelauk/covidhackathon. Additionally, all the software described in this manuscript has been made available using Docker  (Merkel, 2014)  containers. As part of our future work, we aim to make a similar pipeline available for the analysis in Project 2.
Acknowledgement We would like to thank the clinical personnel who are currently making an extraordinary effort to take care of patients in such a challenging time, everyone who has contributed to research in COVID-19 and those who participated in the BioHackathon, especially within the  Gene Expression group. We would also like to acknowledge the computational resources shared with us during the hackathon: a CentOS node with shared storage and a virtual Slurm cluster was provided by the  IT Center for Science (CSC), ELIXIR Finland , and an AWS S3 bucket was.
Data, GitHub repositories and reproducible workflows Following the FAIR principles, data, code and reproducible workflows have been made publicly available under open licenses (MIT license for code, CC0 license for data). Specifically, code, software and containers created as part of this effort are available at https://github. com/ avantikalal/covid-gene-expression (Commit 6a29861e66911a6cbfb709c99002d3255aa88fbd). Furthermore, all relevant data generated here have been deposited at Zenodo. See DOI 10. 5281/zenodo. 3748026 for links to all individual datasets and to a snapshot of the previous code repository. The software developed is registered under the MIT license and the data generated under a CC0 license.
In particular, the sensitivity of indicators, i. e. , their capacity to vary significantly in response to a given driver, and their responsiveness, i. e. , their capacity to respond rapidly to drivers, are properties that are highly desirable for ecological indicators  (Fu et al. , 2019). Recently, the GOOS Biology and Ecosystems Panel considered biomass indicators as being Fish Essential Ocean Variables  (Miloslavich et al. , 2018). Previous multi-ecosystem comparisons based on either empirical data  (Fu et al. , 2012)  or ecosystem simulation data  (Fu et al. , 2019)  revealed that biomass indicators tended to be more environmentally driven but less sensitive to fishing pressure. Comparisons among the nine ecological indicators in the present study identified total biomass as the most consistent indicator over time and the best indicator for tracking the response of ecosystem components to the three drivers of change (fishing mortality, plankton biomass, and marine mammal biomass).
ACKNOWLEDGMENTS We are extremely grateful to the following colleagues (listed alphabetically) who have generously provided different types of data:  Leslie Barton ,  Jennifer Boldt ,  Jaclyn Cleary ,  Kristen Daniel ,  Thomas Doniol-Valcroze ,  John Ford ,  Robyn Forrest ,  Moira Galbraith ,  Chris Grandin ,  Kendra Holt ,  Jim Irvine ,  Sheena Majewski ,  John Morris ,  R. Ian Perry ,  Ian Stewart ,  Matt Thompson ,  Marc Trudel , and  Strahan Tucker.
Description of ASCON ASCON is a symmetric key algorithm for authenticated encryption and the mode of operation is based on sponge duplex modes. The ASCON encryption is updated in four steps: Initialization, Associated data Processing, Plaintext / Ciphertext Processing, and Finalization. The same permutation function is used in all the steps. ASCON has a strong permutation function that takes place between the initialization and the finalization stage. The permutation function consists of a round-dependent constant addition, a substitution layer, and a diffusion layer. The cipher has five inputs which consist of: a variable-length plain text, a variable-size associated data, a fixed-length secret message (which can also be zero), a constant-size public message, and a fixed-size key. ASCON uses a cryptographic duplex construction for encryption and a sponge construction for the absorption of the associated data.
Our Implementation Our ASCON-128 design (see Figure  2 ) has been implemented using VHDL on FPGA device. The practical verification simulations of these designs have been carried out with Mentor Graphics ModelSim. The initial input to the ASCON cipher is five 64 bit states and the state is composed of five gated clock shift registers with independent shift enabled inputs. All state registers are activated and shifted bit-slice-wise through the single S-box instance for calculating the S-box. Plaintext is loaded in the first clock cycle, and then all intermediate states are computed in the next cycles; data is processed through multiplexer in order to correctly route the information at the right time. First, data is stored in the Encryption Register and combined with the round constant. Next, the mixed state is transferred to the S-box layer, and then the linear diffusion layer (which supplies 64-bit data concurrently to the p-layer) and is finally transmitted through the multiplexer back to the Encryption Register.
e. , threshold implementations, with an error detecting approach against fault injection. The purpose of our research is to investigate the effect of clock glitches on AS-CON lightweight cipher. For this we implemented ASCON on an FPGA device and supply the synchronous clock using Digital Clock Manager (DCM) modules. In order to inject an error in the ASCON computation, we generate a clock glitch in an S-box of ASCON. Our research goal is to analyse the effects of fault injections, taking our hardware implementation of the lightweight cipher as use case.
Evaluation The device utilization summary of hardware implementation of ASCON and fault injected ASCON is given in Table  1. From the table, fault injected AS-CON consumes significant resources than ASCON. Power consumption of fault injected ASCON was observed to be nearly equal to ASCON. In our design, clocks are generated by global clock network driven by global clock multiplexers (BUFGMUX) that can multiplex between two global clock sources or be used as a simple BUFG clock buffer. The first encrypted data block of ASCON cipher takes latency of 85 clock cycles. Figure  5  shows the delay comparison between ASCON and its fault injected version in each of the rounds. For the basic ASCON implementation, the latency of each round is between 0. 3s-0. 5s; on the other hand, the implementation augmented with clock glitching injection attacks shows delay nearly equals to gate delay of basic ASCON, as its latency ranges from 0.
This work proposes a methodology for multivariate dynamic modeling and multistep-ahead prediction of nonlinear systems using surrogate models for the application to nonlinear chemical processes. The methodology provides a systematic and robust procedure for the development of data-driven dynamic models capable of predicting the process outputs over long time horizons. It is based on using surrogate models to construct several Nonlinear AutoRegressive eXogenous models (NARX), each one approximating the future behavior of one process output as a function of the current and previous process inputs and outputs. The developed dynamic models are employed in a recursive schema to predict the process future outputs over several time steps (multistep-ahead prediction). The methodology is able to manage two different scenarios: 1) one in which a set of input-output signals collected from the process is only available for training, and 2) another in which a mathematical model of the process is available and can be used to generate specific datasets for training.
Although different algorithms have been developed to automatically select ANNs structures and configurations  [22] , their application requires additional computational effort, since they solve a complex optimization problem, in which the network configuration and its parameters are treated as decision variables to be tuned to minimize an objective associated to the output prediction error  [23, 24, 25]. As a result, their application to cases involving high dimensional systems, large-scale databases and/or online fitting and updating has been quite limited. In spite of these difficulties, a significant number of successful applications of ANNs for dynamic modelling are reported over a wide spectrum of fields  [11, 26, 20, 27]. Especially in the process engineering area, ANNs have been extensively used as Nonlinear AutoRegressive eXogenous (NARX) models for dynamic system identification of both univariate (single output)  [28, 1, 29, 30, 31, 21]  and multivariate (multi-output) problems  [16, 32, 17, 33, 14, 34].
In an area more related to process and system engineering, Boukouvala et al. [12]  proposed a similar approach based on a set of Kriging metamodels, each one predicting the future values of one process output through recursive prediction over several time steps, and applied it to the simulation of a powder-roller-compaction pharmaceutical process. The approach has shown good accuracy in the identification of the dynamic behavior of the process outputs (ribbons density and roll gap) that are influenced by three control inputs (roll speed, roll pressure, feed speed); they proposed the use of a full factorial design for selecting the initial training dataset, and a sequential procedure to update the trained models during their online usage by adding to the initial training set the predicted instances for which the summation of the OKs estimated variances/errors was lower than a specific threshold.
Many DOCE techniques have been developed basically for static surrogate modeling. The most common techniques include Latin hypercube sampling  [44] , low discrepancy sequences as the Hammersley technique  [58]  and space-filling designs like max-min techniques and space-filling Latin hypercube design  [59]. Alternatively, sequential or adaptive sampling are special type of DOCE techniques that are typically related to the use of OK/GP models  [7]. In these sequential techniques, the total number of training points are not selected at once: the surrogate model is initially fitted with a relatively small number of training points, and it is iteratively updated by adding new training points of interest (infill or update points) to the initial training dataset so as to enhance a desired criterion of the metamodel performance, which is highly dependent on the eventual use of the metamodel (global approximation, surrogate-based optimization, etc.
e. not in the training data), [r]n×1 is the vector of correlations between the new point to be predicted x * and the original training data points, and calculated as R (xi, x * ), [R]n×n is the correlation matrix between the training inputs and  [1] n×1 is the identity vector  [60, 2]. 𝑤 ̂(𝑥 * ) = 𝜇 𝑜𝑘 + 𝑟 𝑇 𝑅 -1 (𝑊 -𝟏𝜇 𝑜𝑘 ) (1) 𝑠 ̂2(𝑥 * ) = 𝜎 𝑜𝑘 2 (1 + 𝜆 -𝑟 𝑇 𝑅 -1 𝑟 + (1 -𝟏 𝑇 𝑅 -1 𝑟) -1 (𝟏 𝑇 𝑅 -1 𝟏) ⁄ ) (2) The OK implementation proposed by Forrester et al. [44]  is considered in this paper due to its high effectiveness, generality and ease of implementation. The "fmincon" algorithm of the Matlab optimization toolbox library is used for tuning the OK parameters by maximizing the concentrated likelihood function. The parameters pl are usually set to value of 2, which guarantees smooth differentiable correlation functions  [44].
A set of multivariate dynamic models is to be constructed, which describes the step-ahead evolution of the tanks levels ℎ 1(+1) , ℎ 2(+1) , ℎ 3(+1) , see Eqs. (9). The same general procedure described in Section 3 and the application details illustrated in Section 4. 1 are systematically followed in this case, too. The set of dynamic models in Eq. (  9 ), [ℎ ̂1(+1) , ℎ ̂2(+1) , ℎ ̂3(+1) ], is trained using each type of the training datasets, based on the different considered techniques (i. e. OK and ANNs) and different lags. The same setting and guidelines used in Section 4. 1 for selecting the ANN structure, for customizing its configurations and for tuning the OK models are also considered here.
have provided the best performances, finding NRMSE of 0. 3% and 2. 5%, respectively. Again, the models trained using data generated by the proposed DOCE procedure exhibit enhanced performance with respect to those trained by the data generated using the input-output signal. Figure  16  shows the training data collected by the input-output signal generation (red crosses) and the proposed dynamic DOCE (cyan circles) procedures projected onto some of the metamodels input dimensions. The Figure shows that when the methodology is used for approximating a complex FPM, it is capable of efficiently generating all the possible combinations of the process variables values by the proposed DOCE procedure, in order to collect dataset covering the entire domain of the models input and, consequently, to enhance its prediction accuracy.
However, when the methodology is meant to be applied to a real process, the FPM model is considered as a process plant, but with only few Finally, it should be mentioned that in all the analyzed cases but, especially, in situations where only few input-output signals are available for the training and/or they may represent a biased or partial view of the process (as in the last case study, see Figure  16 -(e,f), red crosses), the resulting dynamic models may be very sensible to the eventual evolution of the real process behavior through the time, which may drive it to unexpected/unexplored conditions, either due to the natural evolution of the process (e. g. : heat exchanger fouling, process aging, drifting, etc. ), or because a wrong/incomplete selection of the training dataset. In such situations, the dynamic models can perform poorly, because they are going to be applied outside the domain of knowledge/information on which they have been trained.
The training of the ANN is accomplished by solving a nonlinear optimization problem, in which an objective function (related to the errors between the predicted outputs by the network and the target outputs of the training dataset) is minimized by tuning the optimization variables values represented in the weights and the biases of the neurons [26, 1]. Amongst the various kinds of ANNs, we consider the Multi-Layer Perceptron ANN (MLP-ANN), which is the most popular kind used in engineering practice, as it offers high efficiency, accuracy and straightforward applicability [55, 1]. The "feedforwardnet" function of the Matlab ANN toolbox is used in this work to build MLP-ANN; a trial and error technique is employed for selecting the suitable number of layers and neurons and the training algorithm achieving a compromise between the structure simplicity and the prediction accuracy.
3. Similarly, in this case, a Gaussian noise with the same mean and standard deviation is added to the all process output data. Finally, the input-output training data matrices, [ () ,  (-1) ,  () ,  (-1) ,  () ,  (-1) ] 300 -[ () ,  () ] 300 , are obtained. Both training datasets (input-output signals or DOCE) have been used to train different groups of the multivariate models in Eqs. (7) , considering the OK and ANNs techniques and various lags ( = 0, 1, 2  3).
This article presents a "proof of concept" based on virtual experimentation: in a discrete 3D space, a simple scene, composed of spheres of different colors and cameras, is modelled in a 3D format. In this work the positioning of the spheres and cameras is limited to a plane. Cameras are placed in the scene in order to see the spheres and then for each camera an image is generated. The application reads each image and deducts relationships between object and camera. These relationships based on the visible occlusions between the projections of the objects onto the photographs, are formalized according to Allen's relationships. A knowledge base is implemented to allow an iterative process of SPARQL queries for qualitative spatial reasoning leading to a set of possible solutions. Finally, the system deduces the relative positions between objects and cameras and the result is imported and can be used within several photogrammetry software suites.
I. INTRODUCTION The transition to the Digital Age has changed many technologies in recent decades. Photography, photogrammetry and computer vision have also been enormously impacted. Towards the end of the 20th century, photogrammetry software suites, reserved for professionals, cost several tens of thousands of euros. Now there are free online services, for example Arc3D 1  [1] ,  [2] , very good open-source software (MicMac 2 , COLMAP 3  [3] ,  [4] ) as well as very efficient commercial software for a tenth of the price. Since the first years of this century, the confluence of photogrammetry and computer vision has given rise to an almost mature discipline. In a way, the construction of a 3D facsimile of a 3D scene by photogrammetry is no longer a matter of research Emerging research topics currently focus either on real-time surveys or mainly on the control of a survey's semantic component.
A particular conceptual framework along with the associated ontology is the optimal way to create a formal representation fit for different abstraction level. An ontology is a set of data elements within a domain that are linked together to denote the types, properties, and relationships between them. In other words, as depicted in  [68] , the main features of ontologies are: (i) Entities and relations, i. e. referred to by a standard identifier such as an Internationalized Resource Identifier (IRI), a Uniform Resource Identifier (URI), that enables data integration since the same identifiers can be used across multiple databases, files, or websites; (ii) Domain vocabulary, i. e. a list of terms associated with the ontology's concepts and relations that can be exploited for applications ranging from natural language processing, creation of user interfaces, etc.
A direct connection between Photograph and Camera(s) through the relation hasCamera allows to use a calibrated camera for a set of photographs. This modeling approach was initially thought to solve interoperability problems between traditional photogrammetry software (Bingo  [70] , Photomodeler  [71] , Agisoft  [72] , OpenCV  [73] ). Indeed, the use of an ontology allows an easy adaptation between various formalisms. However, in recent years, the evolution of information and communication technologies has stimulated the recovery of spatial, temporal, and spatio-temporal data from moving objects. We are now considering an alignment of this ontology with the ontologies managing semantic trajectories  [74]. The concept of Camera considers digital cameras but also cameras using film with fiduciary marks.
B. CDR Knowledge Base An ontological knowledge base can be seen as a structured system of fundamental concepts and relationships and of an agreed epistemology  [76]. In other words, it can be seen as a graph of concepts and data connected with typed relations. Ontology can be used to guide the design of knowledge-based applications and to store the various experimental data as well as the reasoning process in a knowledge manner. Accordingly, the key contribution of this paper is the population of the Arpenteur ontology by the CDR information in order to produce a knowledge base (KB) where the reasoning is performed by an iterative SPARQL query over the CDR KB. The KB contains a schema part (TBox) and a data part (ABox). The schema part is represented by the photogrammetry ontology Arp (stand for Arpenteur) which is serialized in an OWL2 ontology representation.
VI. CONCLUSIONS AND PERSPECTIVES A. Implementation and performance The proposed system software is developed in the Java programming language. The generation of images, 3D models and ontology management are based on the ARPENTEUR platform (See the Arpenteur website  [81] ). The orientation steps described in Section V. Aconsist of ensuring that images are correctly oriented by a coherence check performed via querying the ontology model by SPARQL. These orientation steps generate an ontology of all CDRs orientations relations (graph pattern) of the cameras and the viewed objects. The positioning step described in Section V. Bconsists of querying the produced ontology to find a matching pattern in the grid plane. Since SPARQL querying is the key concept of our system, we implemented an RDF triplestore -Apache Jena Fuseki server  [82]  offering a persistent storage of the ontology and a W3C SPARQL 1.
1  7  quering the stored data. Essentially, SPARQL is based on matching graph patterns against RDF graphs. Graph patterns are based on triple patterns. RDF graphs represent the ontologies. The SPARQL querying process works by transforming the query into a subgraph matching query over the stored RDF graph (ontologies). In other words, answering a SPARQL query means finding all instances (subgraph) from a given data graph matching the pattern specified by a query graph. This particular subgraph matching feature fits perfectly into our development strategy of matching the graph pattern extracted from images (graph pattern of cameras and the viewed objects) into the grid plane in order to find the corresponding match of tiles graph patterns. 1567620 312 ms The system's performance was evaluated using a PC with a CPU of i7-8700k where the process of images orientation (including ontology development, consistency checking via SPARQL, image processing and CDR fact generation) takes around 14 seconds.
C. Photogrammetric results The application presented here infers the positions of the spheres observed in the photos as well as the positions and orientations of the cameras in a discrete space. In this way, this application is very close to a photogrammetry application but does not use the homologous points extracted from the images. Apart from the non-existence of the points, we can easily translate the results to standard photogrammetry software. We have opted for two softwares suites: Photomodeler from EOS and Metashape from Agisoft. These software suites were chosen due to their ease in introducing external data. For Photomodeler, the interface foresees it and for Metashape, in the latest version (1. 6), a JAVA binding is proposed, which makes it easy to communicate with the LBA developments also in JAVA.
We have developed a JAVA bridge between the results proposed by LBA and Metashape version 1. 6. The results are shown in Fig. 11. The cameras and photos have been imported and Metashape is able to display them as well as the 3D model of the calculated spheres. We also added the 3D cameras in the final model imported in Metashape. The result is fully satisfying even if Metashape itself cannot orient such a model at all: there are no 2D or 3D points and no homologous points can be extracted from the photographs because the colors of the discs are completely homogeneous. Fig. 12  shows the use of the orientation results produced by the LBA in Agisoft's Metashape software. This figure shows new 3D points, markers identified manually on the photographs as the centers of the color discs. These points were then calculated in 3D and represented in the model reconstructed in Metashape.