dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014). The third approach is based on analyzing genomic datasets to develop new targeted drug candidates from scratch for precision medicine  (Dyall et al. , 2014). Therapeutic agents developed against coronavirus via the third strategy exhibits promising pharmacological potential. However, this approach's long-term process and expenses are major limiting factors. Herbal medicine has been an alternative medicine since time immemorial in managing various diseases and may be an important source of anti-coronavirus agents  (Ling, 2020). An earlier systematic study in 2003 shows that patients infected with SARS-CoV-2 treated with traditional Chinese medicine (TCM) were reported to have short time hospitalization, reduced steroid side effects, and improvements from the viral symptoms  (World Health Organization, 2004).
Molecular docking studies Theoretical approaches used to compute compounds' chemical and biological activities have been well documented. The quantum chemical (QM) calculation was executed using the MOPAC 2016 software program  (Stewart, 1990). PM7 semi-empirical Hamiltonian incorporating an implicit COSMO solvation model was used to perform the calculation  (Klamt and Schuurmann, 1993; Dutra et al. , 2013). Notably, geometric pre-optimization of the top four inhibitors was carried out using the molecular mechanics force field (MMFF94) integrated with the Avogadro v 1. 2. 0 software program  (Hanwell et al. , 2012) , coupled with chemical structure protonation at a pH of 7. 4. The pre-optimized geometry serves as a query for QM calculation  (Chukwuemeka et al. , 2021). A Broyden-Fletcher-Goldfarb-Shanno (BFGS) geometry optimizer was used for structure minimization and optimization at the semi-empirical theory level.
The keywords "DIPOLE" and "MULLIK" were used to compute the dipole moments and Mulliken atomic charges, respectively. The time-dependent Hartree Fock (TDHF) was used to calculate the molecular polarizabilities using the "POLAR" keyword incorporated in MOPAC 2016. A visualization tool (Jmol software program) was used to visualize the charge distribution diagram of frontier molecular orbitals (FMOs) and molecular electrostatic potential (MEP) of the docked compounds  (Hanson, 2010). A quantum chemical calculation via density functional theory (DFT) was used to investigate the physicochemical properties of lead phytocompounds with the best conformer distribution. All the quantum chemical reactivity descriptors were computed from the energies of the highest occupied and lowest unoccupied molecular orbitals (EHOMO-LUMO).
Protein and ligand preparation The crystal structures of the molecular targets RBD of spike glycoprotein (PDB: 6MOJ), 3CLPro (PDB: 6M2N), PLPro (7CJM), and RdRp (7D4F) were obtained from the protein data bank (https://www. rcsb. org/). They were prepared using Schrödinger's protein preparation wizard  (Sastry et al. , 2013). Hydrogen bond optimizations, water removal, protein structure correction, and ultimately protein energy minimization using the OPLS_ 2005 force field were carried out during the preparation. The position of the co-crystallized ligands for each target was used to define the protein binding pocket for receptor grid generation. Subsequently, the 3D structure of 1,000 compounds consisting of the substructure of the co-crystallized ligand of the targets was downloaded from the PubChem database (https://pubchem.
Molecular docking High-throughput virtual screening (HTVS) of the prepared phytocompounds library was performed using the HTVS module of Maestro integrated into the Schrodinger suite  (Friesner et al. , 2004). The HTVS module used the 3D crystallographic structure of the therapeutic targets and fitted the ligands based on their structural conformations. During the virtual screening process, an energy score of -5. 0 kcal/mol was set as the threshold to identify potential anti-coronavirus agents. The hits generated, 105 compounds out of 1,000 screened phytocompounds, were subjected to molecular docking by considering the flexibility of the protein using the SP (standard precision) model. The compounds were subjected to XP (extra precision) docking using the GLIDE XP module incorporated in Maestro to achieve further accurate results based on binding affinity and pose.
Drug likeness and ADMET evaluation To compute the lead compounds' physicochemical parameters and pharmacokinetic models, the compounds inputted structures were transformed into their canonical simplified molecular input line entry system (SMILES) form. Therefore, the curated ligand database's SMILES were uploaded to the admetSAR web server (http://lmmd. ecust. edu. cn/) (Cheng et al. , 2012)  and SwissADME  (Daina et al. , 2017). Drug-likeness is a method for determining if a therapeutic agent is appropriate for orally active medications. Lipinski's rule of five principles are used to compute in silico predictions based on parameters such as molecular weight, hydrogen bond donor, and hydrogen bond acceptor  (Lipinski et al. , 2001).
Molecular dynamics simulation Molecular dynamics (MD) simulations were conducted to predict the protein's dynamic motion and stability at the atomistic level with the bounded protein. The DESMOND module integrated into the Schrodinger suite was used to generate protein-ligand topologies and trajectories. The protein-ligand complexes were performed for 100 ns with an OPLS3 force field, using the DESMOND version of  Schrödinger (2018). The solvation box was designed as the shape of the rhombic dodecahedron type and solvated using the TIP3P (transferable intermolecular potential 3 point) and an orthorhombic box (10 Å × 10 Å × 10 Å buffer) water model. Na + and Cl -ions in 0. 15 mM concentration were added to neutralize the charge of the systems during simulation. The system minimization tool in the Desmond-Maestro interface was used for energy minimization of the complete system under default parameters of 1.
It takes thousands to several million steps and involves intra-and interatomic interactions simulated simultaneously, for which supercomputers play a vital role in attaining so. It is essential to study the simulation in the order of shortest duration, preferably femtoseconds, because biomolecules' structural and functional properties concern nano-and microseconds  (Liang et al. , 2007). After the chemical profiling, the association of compound complexes was examined, and the dynamic stability of screened compounds was studied using MD simulation at 100 ns in terms of root-mean-square deviation, root-mean-square fluctuations, and molecular contacts (Figures  5 6 7 ). This was achieved with the aid of the Desmond module integrated into the Schrodinger suite. Analyzing the molecular dynamics simulation at the atomistic level, all the compounds were relatively stable through the MD simulation period.
Funding This study was supported by the researchers of  King Saud University, Riyadh ,  Saudi Arabia Supporting  Project number ( RSP-2021/97 ). MDW thanks the  French Agence Nationale de la Recherche  and the  Region Pays de la Loire  for financial support on Covid-19 research (ANR Flash COVID 19 call -name: CoV-E-TARGETgrant number:  2020 07132 ). Princess Nourah bint Abdulrahman University Researchers Supporting Project number ( PNURSP2022R39 ),  Princess Nourah bint Abdulrahman University, Riyadh ,  Saudi Arabia.
01, 0. 5] and generate 10 random networks per value of r. (1) κ β (i, j) := 1 - W β (m i , m j , ω ij ) d ij , (2) w (t+1) ij := d (t) ij -κ (t) β (i, j) • d (t) ij , Results. To evaluate the performance of our method in recovering the communities, we use the Adjusted Rand Index (ARI)  63. ARI compares the community partition obtained with the ground truth clustering. It takes values ranging from 0 to 1, where ARI = 0 is equivalent to random community assignment, and ARI = 1 denotes perfect matching with the ground truth communities, hence the higher this value, the better the recovery of communities. A more detailed presentation of this metric is given in the Supplementary Information. We test our algorithm for different types of information spreading in our OT-based model, as controlled by the parameter β , using the software developed in 64 (available at https:// gitlab.
Analysis of real networks. Next, we evaluate our model on various real datasets  67  containing node metadata that can be used to assess community recovery. While failing to recover communities that align well with node metadata should not be automatically interpreted as a model's failure  68  (e. g. the inferred communities and the chosen node metadata may capture different aspect of the data), having a reference community structure to compare against allows one to inspect quantitatively difference between models. These real networks differ on structural features like number of nodes, average degree, number of communities, and other standard network properties as detailed in Table  1. Specifically, we consider (i) a network of co-appearances of characters in the novel Les Misérables 69 (Les Miserables).
Nodes represent the books, and the edges between books are frequent co-purchasing of books by the same buyers. Books are clustered based on their political spectrum as neural, liberal, or conservative. OT-based algorithms outperform other community detection algorithms in detecting communities aligned with node metadata for two of the four studied datasets, as shown in Fig. 5. In particular, ORC-Nextrout has the highest accuracy performance considering the best performing β in these cases. The impact of tuning this parameter is noticeable from these plots, as the best-performing value varies across datasets. In Les Miserables and Dolphins networks, β < 1 has better performance, while in American Football the best performing value is for β > 1. Performance is similar across OT-based methods in the Political books network.
In Fig. 6  we show the communities detected by the best-performing ORC-Nextrout version together with OTDSinkhorn and Infomap in Les Miserables and Political books (see Supplementary Material for the remaining datasets). Focusing on Les Miserables, we see how ORC-Nextrout successfully detects three characters in the green communities, in particular a highly connected node in the center of the figure (in dark green). Notice that these are placed in the same community (pink or black) by OTDSinkhorn. Thus ORC-Nextrout achieves a higher ARI than OTDSinkhorn. Both OT-based approaches retrieve well communities exhibiting clustering patterns with many connections within the community. Instead, they both divide the communities with a hub and spokes structure due to the lack of common connections within the group.
The communities detected in both datasets highlight the tendency of OT-based methods to extract a larger number of communities than those observed from node metadata. Among these extra communities, some are made of a few nodes (e. g. the light-blue and violet), while others are made of one isolated node each (highlighted Figure  4. Example of community structure on a synthetic LFR network. The rightmost panel shows the ground-truth community structures to be predicted in an LFR network generated using µ = 0. 35. This network is one sample of the synthetic data used in Fig. 3. Square-shaped markers denote nodes that are assigned to communities different from those in ground-truth. In the middle and last panels, ORC-Nextrout with β = 2 perfectly retrieves the 21 communities, while OTDSinkhorn predicts only 19 communities with an ARI score of 0.
Two tests on semi-synthetic networks. To further investigate the different performance gaps between the various approaches, we expand the comparison between the OT-based methods and Infomap on two semisynthetic scenarios generated from Les-Miserables (Fig. 6a ) and Dolphins datasets, where the largest ARI differences were observed. Specifically, we add random noise to the existing set of connections to understand if the performance gap can also be observed in more challenging scenarios. We add noise to the real data in two different ways (more details can be found in the Supplementary Material): 1. Flipping entries: from a given network, we generate a new one by flipping R entries of its adjacency matrix A uniformly at random. This means that if A ij = 1 , this is changed to A ij = 0 , and vice versa.
We generate 20 samples for each of these two mechanisms built using the Les-Miserables dataset ( |V | = 77 , |E| = 254 ) shown in Fig. 6a. We show the results obtained in the test of removing intra-community edges as scatter plots in Fig. 7. We use these plots to compare the algorithms on trial-by-trial community detection tasks: a point on each plot is an instance of a semi-synthetic network with (x, y) coordinates being the ARI scores of ORC-Nextrout (y) and either OTDSinkhorn or Infomap (x). If y > x , then ORC-Nextrout outperforms the other method in this particular dataset (blue markers), and vice versa if x < y. We then compute the percentage of times that ORC- Nextrout outperforms the other (as indicated in the legend). We find that ORC-Nextrout outperforms both OTDSinkhorn and Infomap clearly and consistently across different values of r ranging from r = 0.
In this work, our aim is to provide a structured answer in natural language to a complex information need. Particularly, we envision using generative models from the perspective of data-to-text generation. We propose the use of a content selection and planning pipeline which aims at structuring the answer by generating intermediate plans. The experimental evaluation is performed using the TREC Complex Answer Retrieval (CAR) dataset. We evaluate both the generated answer and its corresponding structure and show the effectiveness of planning-based models in comparison to a text-to-text model.
OR Fig. 1. Example of a query from the CAR dataset  [6]  and variants of outputs (structured or plain answers) obtained using a sequential DTT planning-based model. natural language form  [14, 18, 16]  answers to open-domain questions. Most opendomain QA approaches adopt the "Retriever Reader" framework: the retriever ranks candidate passages, then the reader constructs the response based on the most relevant retrieved candidate  [9, 17, 24]. Compared with open-domain QA, answer generation to open-ended queries has two main specific issues: 1) all the documents provided by the reader potentially contribute both as evidence and source to generate the answer leading to difficulties in discriminating between relevance and salience of the spans; 2) while most QA problems target a singlespan answer  [22]  included in one document, open-ended queries are characterized by multiple facets  [19, 20]  that could target a multiple-span answer.
The latter is obtained by the planning encoderdecoder since the T5 model provides embeddings for both documents independently and the set of documents. After the encoding, the network then decodes an answer a. The training is also guided by the auto-regressive generation loss: L answer (q, a, p) = P (a|q, p, D q ) = |a| k=1 P (a k |a <k , q, p, D q ) ( 2 ) where a k and a <k resp. express the k th token in answer a and the token sequence of answer a before the k th token. The final loss is then a combination of both auto-regressive losses: L = {q,a,p}∈Q×A×P L planning (q, p) + L answer (q, a, p) 3 Evaluation Setup Dataset. We selected the TREC CAR (Complex Answer Retrieval) 2017 corpus  [6]. This dataset includes: (1) queries -denoting complex search tasks with multiple facets, (2) plans -expressing the different expected facets, and (3) paragraphs extracted from English Wikipedia -corresponding to texts associated with plan sections.
The TREC CAR task consists of retrieving the paragraphs associated to each plan section to build a structured answer combining both plan sections and paragraphs. We used these structured answers as the final objective of our generation model given the queries; and the plans as the structure prior. Due to the structure prior constraint, we removed in the training set 2  answers without any plans. To compare the models abilities to generate structured answers, we also evaluate a new form of expected answer (plain answers) where structure is not taken into account. For this aim, we built a new dataset upon the initial TREC CAR dataset but only considering the paragraphs (without plans). Thus, we obtain two versions of datasets (for structured answers and plain answers) which both follow the original split of the TREC CAR dataset.
Second, for computational reasons, we reduced the number of entries in our training set by considering only a half of Fold 0 of the original dataset. Also, due to the memory constraints of generation models and the length of Wikipedia articles, we reduced the document size by only keeping the first sentence of paragraphs. Some statistics of the original dataset and our two adapted datasets are given in Table  1. Model variants and baselines. We implement two versions of our model 3  : • Planning-seq: a sequential model where the planning module (Equation  1 ) and the content generation module (Equation  2 ) are trained separately. At inference, both modules are used sequentially. • Planning-e2e: the end-to-end version of our model (Equation  3 ). The content module is fed with the output embeddings of the planning module, and document tokens.
Besides, we compare our models with two baselines: 1) the T5 model  [12]  which is fine-tuned on each dataset, and 2) Ext, an extractive method where we extract, for each sentence in the ground truth, a sentence in the input supporting documents that maximizes the F1 score of BERTScore  [23]. All models consider for each topic a set of 10 relevant paragraphs ranked using BM25 as input. Metrics. To evaluate the quality of the generation, we consider three well-known metrics: 1) the ROUGE-L mid metric (Rouge-P, Rouge-R, Rouge-F)  [8]  measuring the exact match between the generated and the reference texts, 2) the BERTScore  [23]  (the F1 score is reported) which computes similarity between the generated and the gold reference text embeddings, 3) the QuestEval  [13]  framework which relies on question answering models to assess whether a summary contains all the source information: if the same questions are asked to the generated and the reference texts, the produced answers should be consistent.
Conclusion Traditionally, IR approaches solving complex information needs focused on leveraging multi-turn interactions to provide optimal rankings of candidate documents at each turn. In this paper we have suggested alternative retrieval models that do not rely on the interactive updating of queries and document rankings as answers. We suggest one such alternative approach can be found using datato-text generation models to generate in a single-turn, a natural language and structured answer. Experimental evaluation of a planning-based DTT model using the TREC CAR dataset shows the potential of our intuition. We believe that our work opens up novel areas of investigation including answer generation and explanation in conversational systems for IR.
CFD solvers CFD simulations for the NACA hydrofoil and the DTMB 5415 are performed with the URANS solver ISIS-CFD developed at ECN -CNRS  [29] , available in the FINE/Marine computing suite from Cadence Design Systems. The hydrodynamics performance of the RoPax is assessed by the URANS code χnavis developed at CNR-INM  [7, 8, 4].
Acknowledgements CNR-INM is partially supported by the  Office of Naval Research  through  NICOP  grant  N62909-21-1-2042 , administered by  Dr. Woei-Min Lin ,  Dr. Elena McCarthy , and  Dr. Salahuddin Ahmed  of the  Office of Naval Research and Office of Naval Research Global , and the  EU  funded project  HOLISHIP (HOLIstic optimisation of SHIP design and operation for life cycle ), grant agreement N. 689074. The development of the methodology is conducted in collaboration with the NATO STO AVT task group on "Goal-driven, multi-fidelity approaches for military vehicle system-level design" ( AVT-331 ).
The weakness of simple anonymization was demonstrated by L. Sweeney in  (Sweeney, 2002)  and was further confirmed by Y. A. de  Montjoye et al. in (De Montjoye et al. , 2015). By joining two public data sources (anonymized medical insurance dataset of the Massachusetts employees and voter's registration list), L. Sweeney successful identify William Weld, a former Governor of Massachusetts. This attack, named ''linking attack", is shown in Fig. 1. To overcome this problem, more elaborate anonymization approaches were proposed. K-anonymity  (Sweeney, 2002)  is one of those approaches. It is the first model proposed in the literature and remains the most used for privacy protection in micro-data. It requires that each record in a table cannot be distinguished among at least k-1 other records.
The algorithm starts with an initial population of candidate solutions generated randomly. Its objective is to find, based on this population, the optimal k-anonymity solution i. e. , which has the smallest information loss. As our objective is to address limitation of clusteringbased k-anonymity, we consider the initial population of BHA as a set of clustering-based k-anonymous solutions generated by a clustering algorithm. In order to improve the data quality, the clustering algorithm should place similar records (with respect to the quasi-identifiers) in the same group, each group containing at least k records. The similarity is computed based on information loss as a distance and cost metric. This ensure that less distortion is required to anonymize the record in a cluster which enhance data quality.
The OKA starts by defining a metric space of the quasi-identifying attributes over the database records, which are then viewed as points in this space. Then, in the first stage the points are partitioned using k-means algorithm, but only for the first iteration, in a set of groups of size >= k. During the second stage, the sizes of clusters are adjusted to ensure that each cluster contains no fewer than k records. The adjustment is done by moving the records from clusters with more than k records to clusters with fewer than k records. Lin et al. (Lin et al. , 2008)  proposed a hybrid algorithm which combines OKA  (Lin and Wei, 2008)  and the k-member algorithm  (Byun et al. , 2007) , called the Hybrid Method. OKA is used in the first step to generate some clusters with small information loss, and the k-member algorithm in the second step to generate the remaining clusters with less fluctuation of information loss.
Adult (UCI). University (UCI). better Total-IL. worse execution time than Hilbert algorithm and better than the remains compared algorithms He et al. (He et al. , 2012)  proposed a clustering-based kanonymity algorithm. The algorithm is an iterative process. At each round, the data set is partitioned into two subsets G1 and G2 based on Normalized Certainty Penalty (NCP) as a distance measurement. If the size of one of subset is smaller than k, assume that G1 < k, the sizes of the two sub-sets are adjusted by borrowing K-|G1| tuples from G2 to make sure that G1 has a cardinality upper or equal to k. The adjustment continues until each subset contains at least k tuples. Pramanik et al. (Pramanik et al. , 2016)  proposed a clustering approach to achieve k-anonymity called KOC. The algorithm starts with computing the n-closest neighbors for each record and sorts the records in descending order.