dataesr/scientific-paragraphs-categorization huggingface dataset:
E. Robustness against distortion types Since LFI may suffer from different types of distortions during the image processing chain, we aim to investigate the robustness against distortion types of our metric in this subsection. TABLE X shows the experimental results for individual distortion type on the Win5-LID and NBU-LF1. 0 datasets. Two CNN-based distortions in Win5-LID dataset are not considered here since they have only one distortion level. Limited by the paper space, we only show the PLCC results, while the results of SROCC and RMSE are similar. The table shows that the proposed PVBLiF not only achieves the best or second best performance in all distortion types, but also yields significant improvements in some distortion types, such as HEVC. These experimental results demonstrate that the proposed metric has strong robustness when confronting different distortion types.
F. Statistical significance test In the subsection, we perform the F-test to compare the statistical significance between any two LF-IQA metrics following  [73]. Specifically, we first calculate the residuals between the predictions M OS p and their corresponding subjective scores M OS for each metric, Res M = M OS M p -M OS M (15) where Res M denotes the residuals of metric M. Since leave-two-fold-out cross-validation is adopted in our experiments, Res M contains the residual results of all trainingtest splits for a comprehensive significance test. Then the vartest2 function in Matlab is used to calculate the significance relationship of any two metrics, in which the confidence level is set to 95%. The statistical significance results between any two LF-IQA metrics on three datasets are shown in Fig.
9 , where "1", "0", and "-1" indicate that the row metric has better, competitive, and worse statistical performance than the column metric, respectively. From the figure, we can see that on the SHU dataset, the proposed PVBLiF metric significantly outperforms most FR/NR LF-IQA metrics, only our previous metric DeeBLiF has competitive statistical performance to our PVBLiF metric. On the Win5-LID and NBU-LF1. 0 datasets, our PVBLiF metric has significantly better statistical performance than all state-of-the-arts. These demonstrate the superiority of our metric in LFI quality evaluation.
G. Time complexity Before employing an IQA metric to practical applications, we need to consider its time complexity. For fair comparison, all the metrics are executed using the same hardware configurations (CPU: Intel (R) i7-10700 2. 90GHz; GPU: NVIDIA GeForce RTX 3080 10G; Memory: 64G RAM). The test time represents the runtime for testing a single LFI, excluding ACCEPTED MANUSCRIPT / CLEAN COPY  Here, "1", "0", and "-1" indicate that the row metric has better, competitive, and worse statistical performance than the column metric, respectively. the SROCC performance on the Win5-LID dataset, where metrics with and without * denote the runtime using GPU and CPU, respectively. From the table we can find that most NR LF-IQA metrics are time-consuming, because they rely on extracting multiple handcrafted features to ensure the prediction accuracy.
V. CONCLUSION In this paper, we propose a novel Pseudo Video-based Blind quality assessment metric for Light Field image (PVBLiF). First, considering the limitations of 2D representations in reflecting LFI quality, a more intuitive 3D representation, Pseudo Video Block Sequence (PVBS), is used for the quality assessment of LFI. To achieve this goal, we exploit the potential of CNN structure and propose a novel CNN-based network, named PVBSNet, to extract the spatio-angular features of PVBS and predict the PVBS quality. Further, since different regions of the same scene have different visual effects on the human perception, we present a Saliency-and Varianceguided Pooling (SVPooling) method to integrate all the PVBS quality into the overall LFI quality. Finally, to validate the superiority of the proposed PVBLiF metric, we conduct extensive experiments with 25 existing 2D/3D/multi-view/LF IQA metrics on three publicly available LFI datasets.
This work was supported in part by the  National Natural Science Foundation of China  under grants  62101344 ,  62171294 , in part by the  Natural Science Foundation of Guangdong Province, China  under grants  2022A1515010159 ,  2020A1515010959 , in part by the  Key Project of DEGP  under grants  2018KCXTD027 , in part by the  Key Project of Shenzhen Science and Technology Plan  under Grant  20220810180617001.
9040 0. 8848 0. 3853 0. 9060 0. 8883 0. 3746 to some extent. Fig. 8 provides the box plots of SROCC distribution on the Win5-LID and NBU-LF1. 0 datasets. Boxes with smaller sizes and higher positions indicate better generalization and performance, respectively. The red line in the box denotes the median SROCC result. The quantitative median SROCC performance is shown in TABLE III. We can see that the proposed PVBLiF metric achieves better generalization and performance than other state-of-the-art IQA metrics.
CORAL QSAR Model Six QSAR models were built based on three random splits and two target functions: TF1 with W IIC = 0 and TF2 with W IIC = 0. 2. Three splits were created from the entire dataset and each split was divided into four sets, as described below (for definitions of the parameters, refer to the Materials and Methods section (Section 3) of this article). The obtained statistical parameters for the developed SMILES-based QSAR models show that W IIC = 0. 2 increases the effect of IIC on Monte Carlo optimization. Table  1  displays the computed statistical parameters for all splits. Figure  1  depicts the experimental pIC 50 values compared to the estimated values for the three splits. Table  1  clearly illustrates that all models are statistically reliable and fulfill the criteria set by Tropsha et al.
Data Collection, Molecular Structures Preparation and Molecular Descriptor Calculation For this study, the 2D structures of all 55 dihydrophenanthrene derivatives, collected from the published work of Jian-Wei Zhang & al  [17] , were sketched using ACD/ChemSketch software and converted to 3D structures using ChemDraw 16. 0 software. For geometry optimization and partial charge assignment, the MMFF94 force field was employed with the steepest descent as an algorithm and with 1000 as the number of steps used for optimization. Then, the geometries were optimized using the AM1 method in the gas phase implemented in the Gaussian 09 software  [18]. Frequency analysis was checked to investigate the energy minima of the optimized derivatives. The optimized molecular structures were converted into Simplified Molecular Input Line Entry System (SMILES) codes for the modeling process of the CORAL model.
To build the GA-MLR model, alvaDesc software (version 1. 0. 8) was used to calculate the molecular descriptors using the optimized 3D structures  [19]. To avoid multicollinear variables in the QSAR model, the number of generated variables was reduced by eliminating descriptors that had a constant value of over 95%, and by keeping only one of the descriptor pairs that possessed a correlation coefficient higher than 0. 9. The experimental data value of each molecule (half-maximal inhibitory concentration, IC 50 ) was converted into the form of a logarithm (-log IC 50 ). The molecular structures and their corresponding IC 50 data are listed in Table  S5 , and their SMILES notation and converted pIC 50 are available in Table  S6.
SMILES-Based QSAR Model Building For QSAR modeling using CORAL 2019 software  [20] , the entire dataset (55 derivatives) was randomly divided into three splits. Each split consisted of 4 sets: active training (AT, 35%), passive training (PT, 35%), calibration (Cal, 15%), and validation (Val, 15%). Each set has a specific role in the development of the QSAR model, which is well explained in the literature  [21] [22] [23]. The AT set is used to develop the model and generate correlation weights. These weights are then used to compute the descriptors for all the compounds involved in the modeling process. The PT set is used to evaluate the robustness of the model for compounds not belonging to those used to construct the model. The Cal set is used to detect the onset of overfitting. The Val set provides an independent evaluation of the statistical quality of the model using data for compounds that were not included in the model development and optimization.
GA-MLR QSAR Model Building To initiate QSAR analysis, the initial stage is to select the most appropriate descriptors from the complete set of computed ones. For this purpose, alvaDesc software was used to compute 5666 molecular descriptors, and only 643 descriptors were filtered out based on the criteria mentioned above (Section 2. 1). Then, a stepwise MLR method was performed using SPSS software to select the most relevant descriptors  [26]. Finally, 6 molecular descriptors were kept. Using these selected descriptors, the MLR method established a linear relationship between the pIC 50 endpoints of the molecules and their molecular descriptors by applying the ordinary least squares (OLS) algorithm implemented in the QSARINS software  [27, 28]. The data set was randomly divided into a training set (70%, 39 molecules) and a test set (30%, 16 molecules).
Molecular Docking Study In the current study, molecular docking was used to investigate the conformational patterns and ligand-protein interactions of the designed molecules within the CS and DIS sites of the SARS-CoV-2 Mpro. Two 3D crystallographic structures of the Mpro protein were downloaded from the Protein Data Bank (PDB) of the Research Collaboratory for Structural Biology (RCSB; https://www. rcsb. org/, accessed on 10 March 2023) with the PDB ID codes: 6M2N and 6M2Q  [35]. The dimeric structure (6M2N) was used as a receptor molecule to explore the preferred conformation pose of the designed molecules in its active binding site, while the monomeric structure (6M2Q) was used to investigate the best conformational pose of the designed molecules in its DIS. Prior to the docking process, the two protein structures were prepared by removing water and heteroatom molecules, adding polar hydrogens and Gasteiger charges, and saving them in the pdbqt format with the assistance of AutodockTools.
Molecular Dynamics Simulation Details The newly designed compounds which showed stronger binding to the CS and DIS were subjected to all-atom molecular dynamics using the GROMACS 2020. 6 (Groningen Machine for Chemical Simulation) software  [40, 41]. Before running the MD simulation, the CHARMM-GUI web server  [42]  was used to generate the initial input parameters implementing the CHARMM36 force field  [43]. Based on a rectangular grid box, each complex was solvated within TIP3P water and the necessary counterions (Na + ,Cl -) were added to maintain a salt concentration of 0. 15 M through the Monte Carlo ion displacement. Energy minimization of each system was performed using the steepest descent algorithm with a maximum of 50,000 steps and a maximum force of 10. 0 KJ/mol. The temperature and atmospheric pressure were set to 300 K and 1.
01325 bar, respectively. Each system was equilibrated using canonical (NVT) and isothermal-isobaric (NPT) ensembles. After that, the MD simulation was performed for 100 ns. Based on the dynamics trajectory results, the root mean square deviation (RMSD), the radius of gyration (Rg), and root mean square flexibility (RMSF) were used to evaluate the structural stability of the designed molecules within the CS and DIS of Mpro. The obtained data were plotted using Xmgrace software  [44].
MM-GBSA The calculation of the binding free energies of protein-ligand complexes was estimated by the Molecular Mechanics Generalized Born Surface Area (MMGBSA) method using the gmx-mmgbsa package  [45]. The binding free energy is calculated according to the following equations: ∆G bind = ∆G complex -∆G protein + ∆G ligand  (11)  where the total free energy of the complex is represented by ∆G complex , while the energies of the isolated protein and ligand are represented by ∆G protein and ∆G ligand , respectively; ∆G bind = ∆E gas + ∆G sol = ∆E vdw + ∆E ele + ∆G polar + ∆G nonpolar  (12)  where ∆E gas is the average potential energy of molecular mechanics in a vacuum, which includes both van der Waals interactions (∆E vdw ) and electrostatic interactions (∆E ele ). ∆G solv is the contribution to the solvation-free energy that is made up of the polar solvation energy (∆G polar ) and the nonpolar solvation energy (∆G nonpolar ).
ADMET Study The absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis plays a critical role in drug discovery and development. Based on the ADMET results, we could predict the efficacy and safety of the potential hit compound in the early stages of drug development. In this regard, the ADMET predictions for the compounds were performed using the pkCSM server  [46]  for pharmacokinetic properties and the Osiris property explorer software  [47]  for toxicological properties.
In this paper, we propose a method, called AmoCoala, which for a given pair of host and symbiont trees, first pre-estimates the probabilities of spread events directly from the input (relying on heuristic frequencies estimates) and second estimates the probabilities of the remaining four classical cophylogeny events, relying on an ABC approach. In doing so, we also define a new distance to compare two symbiont trees that are associated with the same host tree in presence of multiple associations. Indeed, ABC methods heavily rely on the ability to compare observations with simulated datasets. In the cophylogeny context, this means comparing trees (as these are the most complete information on the data), a task that is far from trivial. Our new distance is an extension of the classical Maximum Agreement Subtree distance (MAS )  (Ganapathy et al.
, 2005)  to what we call set-labelled trees. We believe this new distance can be of independent interest (see Section Model and Method and also Section B. 3 in the Supplementary Material). We test AmoCoala on both synthetic and real datasets and compare the results with Coala. We could not compare our approach with the tool  Alcala et al. (2017)  due to the, previously described, substantial differences both in the model and in the input. Our tests show that AmoCoala produces results that seem closer than those of Coala to what is expected from the judgment of a biological expert. 2 Model and method.
In that case, we say that h 8 is the ancestral host of s 5 and the two clades S s 5 (which denotes the symbiont clade rooted in s 5 ) and H h 8 (the host clade rooted in h 8 ) are related. We say that a vertical spread has happened at symbiont s 5 and we associate s 5 with all the vertices in the subtree rooted in h 8 (see Figure  1 (b)). (s 1 ) = {h 3 , h 4 , h 5 }; φ(s 2 ) = {h 3 , h 4 , h 5 }; φ(s 3 ) = {h 2 }; φ(s 4 ) = {h 1 }. (b) In dotted lines, a reconciliation involving 2 cospeciations in s and s 6 and 1 vertical spread in s 5. More precisely, the reconciliation is given by λ(s) = {h}; λ(s 6 ) = {h 6 } and λ(s 5 ) = {h 3 , h 4 , h 5 , h 7 , h 8 } (on the symbiont leaves, we have λ = φ). Horizontal Spread. In some datasets, we see the occurrence of the same symbiont in two different clades of the host tree.
Formally, we are interested in the posterior distribution p(θ|D 0 ) = p(D 0 |θ)π(θ)/p(D 0 ). For simple models, the likelihood function p(D 0 |θ) can typically be derived. However, for more complex models the likelihood function might be computationally very costly to evaluate. In these cases, ABC methods approximate the posterior distribution by simulations, the outcomes of which are compared with the observed data. First, a population of N parameter values θ i is sampled from the prior distribution. Then, for each sampled parameter θ, a dataset Dθ is simulated. It consists of a simulated symbiont tree Sθ together with a reconciliation λ from Sθ to H. This dataset Dθ is then compared with the real dataset D 0 through a summary measure which is used as a quality measure to accept or reject the candidate parameter value θ.
In many cases when it is believed that the prior and posterior densities are very different, the acceptance rate is very low. To deal with that issue, we can rely more specifically on a likelihood-free Sequential Monte Carlo (SMC) search that involves many iterations of the simulation procedure, each iteration targeting more precisely good candidate parameter values. Given an input dataset (H, S, φ), an ABC-SMC method was developed in Coala  (Baudet et al. , 2015)  to infer the posterior density of the probability of each of the four classical events, namely cospeciation, duplication, host switch and loss. Coala includes two main parts. The first consists in a simulation algorithm of the coevolutionary history of symbionts and their hosts. More specifically, given the host tree H and a vector θ = p c , p d , p s , p l specifying the probability of each of the classical cophylogeny events, the model generates a symbiont tree Sθ together with a reconciliation from Sθ to H describing the ancient host-symbiont associations.
In AmoCoala, this first part is improved by introducing spread events whose probabilities of occurrence are fixed throughout all the simulations, while being heterogeneous along the host tree and specific to the original dataset. More precisely, these probabilities are pre-estimated on each dataset through simple frequency estimates related to the symbiont and host associations. Their values are specific to each node h of the host tree. The second part concerns a method to select the most likely probability vectors based on an ABC-SMC variant. It relies on the main idea that the most likely vectors θ will generate trees Sθ together with reconciliations λθ from Sθ to H that are similar to the original input (H, S, φ). In Coala, the symbiont trees together with their leaf associations were summarised through labelled trees and this step thus relied on a phylogenetic distance between labelled trees.
ABC-SMC inference method AmoCoala is based on the same ABC-SMC method presented in Coala. It is an iterative method with many rounds, and it involves a summary discrepancy that describes the quality of any candidate vector θ (i. e. how much it is susceptible to have generated the observed dataset). We first present Algorithm 2 that describes how we rely on simulated trees in a reconciliation model with spreads produced through Algorithm 1, to characterize the quality of a candidate vector θ = p c , p d , p s , p l with respect to the observed dataset (H, S, φ). In particular, for each candidate vector θ, we produce many different trees and summarise them into a discrepancy d θ that characterizes the quality of θ as a candidate to produce the observed data. The structure of this procedure is unchanged from Coala, except for the way we compute the discrepancy d θ.
Algorithm 2 Symbiont tree simulation algorithm overview We then present a general overview of the ABC-SMC procedure in Algorithm 3. We include all the details of the method in Section B. 2 from the Supplementary Material. Moreover, we report below the differences between this procedure and the one at stake in Coala. The main difference between Algorithms 2 and 3 and their respective corresponding versions in AmoCoala lies in the summary discrepancy d θ used to quantify the quality of the vector θ. The summary discrepancy between a simulated dataset (the generated symbiont tree and its host associations) and the observed one (the real symbiont tree and its host associations) is measured through a distance between phylogenetic trees which can be calculated in polynomial time. Similarly as in Coala, this discrepancy is built from two components: (i) d 1 , that describes how much the simulated tree Sθ is representative of the vector θ, and ii) d 2 that measures how much is Sθ (and its labels) topologically similar to S (and its labels).
Experimental settings Parameter settings. For each (synthetic or biological) dataset (H, S, φ), we ran AmoCoala with the following parameter values. We simulated N = 2000 vectors θ i ,  (1 ≤ i ≤ 2000)  in the first round of simulation. For each vector θ i , we simulated M = 1000 symbiont trees. We discarded the simulated trees whose size is larger than twice the one of the real symbiont tree S in input as being too different from the original one. The tolerance value used in the first round was τ 1 = 0. 1. We ran R = 3 rounds and we defined τ i = 0. 25. Notice that τ 1 × N = 200 defines the size Q of the quantile set which must be produced in each new round. Thus, after the last round, we have τ 3 × Q = 50 accepted vectors. Synthetic datasets generation. Synthetic datasets are obtained in a similar way as in Coala (see  Baudet et al.
, 2015 , for more details), the only difference lying on the fact that the simulation algorithm now includes spread events. In particular, we use the real symbiont tree and its (multiple) associations to the host tree to derive the spread probabilities. To obtain realistic datasets, we started from a real biological tree and chose the dataset SFC described in the next section. This host tree H (and associated spread probabilities) is combined with 8 different parameter values. We thus simulated 8 datasets (H, S θ j , φ j ) for 1 ≤ j ≤ 8 associated with the following 8 probability vectors, in the form θ = p c , p d , p s , p l. We used θ 1 = 0. 70, 0. 10, 0. 10, 0. 10 , θ 2 = 0. 80, 0. 15, 0. 01, 0. 04 , θ 3 = 0. 75, 0. 01, 0. 16, 0. 08 , θ 4 = 0. 70, 0. 05, 0. 02, 0. 23 , θ 5 = 0. 60, 0. 20, 0.
00, 0. 20 , θ 6 = 0. 55, 0. 00, 0. 20, 0. 25 , θ 7 = 0. 45, 0. 10, 0. 15, 0. 30 and θ 8 = 0. 40, 0. 20, 0. 10, 0. 30. The choice of these vectors was done with the aim to cover some typical coevolution patterns of probability. Indeed, vectors with very low probability of cospeciation correspond to situations where there is almost no signal of the coevolution of the species at a macroevolutionary level. In these cases, the cophylogeny reconciliation methods are not appropriate  (Baudet et al. , 2015; Althoff et al. , 2014). Moreover, a high probability of host switches or duplications is not appropriate to produce synthetic datasets due to the variability of the simulated trees. Note that for these reasons, a ninth parameter value used in Coala was discarded here.
Results of the self-test The objective of this test is to check whether AmoCoala produces the correct results for synthetic datasets where we know the truth. To this purpose, we ran Amo-Coala 50 times on each of the 8 synthetic datasets generated as explained in the previous subsection with true parameter value θ i. We expected to find a vector "very close" to θ among the vectors accepted on the last round of AmoCoala. Note that contrarily to what we did in Coala, we here rely on an Euclidean distance over the parameter vectors. At the end of the third round, we therefore took note of the clus-ter whose representative parameter vector had the smallest Euclidean distance to θ i (we call it the "best" cluster). We also stress again that parameter vectors do not include the probabilities of spread events, which are pre-estimated before applying the ABC-SMC approach.
It results in over-estimation of the cospeciation and underestimation of the loss probabilities. Overall, these simulations show that AmoCoala is able to select parameter vectors that are close to the true ones. For each simulated dataset with true parameter value θ 1 = 0. 70, 0. 10, 0. 10, 0. 10 , we ran AmoCoala 50 times and, at the end of the third round, we took note of the cluster whose representative parameter vector had the smallest euclidean distance (histograms shown in the first column) to θ 1. Columns 2 to 5 show the histograms of the distributions of the event probabilities in these "best" clusters. The dashed vertical black line indicates the mean value. The solid vertical red line indicates the true parameter value.