dataesr/scientific-paragraphs-categorization huggingface dataset:
Classification of the caulimovirid EVEs using Branch B To benchmark CAULIFINDER Branch B, the V. vinifera genome database described above was used. We removed the florendoviral RT probes from the default search library and from the library used to filter out false positives in step 1, but not from the library used in step 3 for constructing the phylogenetic trees. In step 1, Branch B detected 3833 loci with significant matches to the caulimovirid RT probes. These sequences contained 1786 ORFs coding for proteins with a minimum size of 200 aa, of which 84 were retained based on their best BLAST hit against caulimovirid RT sequences. In the second step, the conceptually translated protein sequences were clustered on the basis of similarity, using an 80% aa identity threshold, resulting in ten groups.
One representative sequence per group was retained. All representative sequences passed the filtering stages in steps 2 and 3 based on MSA quality assessment with trimAl, resulting in 10 candidate caulimovirid RT protein sequences retained to produce the final alignment in step 3. The output phylogenetic tree showed that CAULIFINDER Branch B succeeded in detecting eight RT sequences clustering in the florendovirus clade, as well as two others that grouped with Vitis endovirus (Fig. 4 ). The main output files of this run are available in the supplementary dataset.
Conclusions This paper describes CAULIFINDER, a package dedicated to the detection of caulimovirid EVEs in plant genomes. CAULIFINDER uses two complementary workflows: Branch A produces a library of consensus sequences, whereas Branch B addresses phylogeny and classification. We have developed CAULIFINDER with three main applications in mind, the first aimed at those studying genome biology, who will benefit from more accurate genome annotations reflecting the presence of caulimovirid EVEs. The second application is relevant to paleovirology, since CAULIFINDER will help unravel cryptic and/or ancient host-virus interactions and inform the evolution of the Caulimoviridae over unprecedented timescales. The final application is in the field of plant pathology, as some caulimovirid EVEs retain replication competency and can give rise to new infections.
All CAULIFINDER librarieswill be updated on a regular basis in the dedicated dataverse (https:// doi. org/ 10. 57745/ ADFNMB) to reflect new discoveries and updates of the taxonomy of viruses. Considering that Branch A searches for repetitive elements at the genome level, it is recommended to use it only with genome assemblies. It is worth noting that sequence decay accumulate over time in EVEs following integration, causing repetitiveness to be lost gradually. This is likely to affect the sensitivity of Branch A. Branch B scans for RT domain diversity and can be launched on genome assemblies, but it is also adapted for mining transcriptomic datasets (i. e. assembled transcriptomes). Branch B comprises a translation step, therefore it is not recommended to use it on uncorrected long DNA sequencing reads produced from PacBio and ONT technologies since sequencing errors could interrupt RT ORFs.
e. , one-step processing) and declared better performance than the two-step processing  (Che et al. 2018; Cui et al. 2020a, b; Tian et al. 2018; Tang et al. 2020). However, the above-mentioned work suffers from three major issues. First, the missing and zero values are usually considered to be the same, leading to unnecessary, even harmful data imputations, thus contradicting the raw data information. Second, most of the work  (Che et al. 2018; Cui et al. 2020a; Tian et al. 2018; Tang et al. 2020 ) considers missing values from the temporal aspect, ignoring the rich information from the spatial perspective. Third, they are generally designed for processing the missing Fig. 1  Missing measures of traffic speed data from METR-LA dataset  (Li et al. 2018). left) long-range missing on entire network (i.
• Spatio-temporal memory module: We propose a memory module that can be used by GCN-M to learn both local Spatio-temporal features and global historical patterns in traffic data for handling the complex missing values. • Dynamic graph modeling: We propose a dynamic graph convolution module that models the dynamic spatial interactions. The dynamic graph is characterized by the learned local-global features at each timestamp, which not only offset the missing values' impact but also help learn the graph. The rest of this paper starts with a review of the most related work. Then, we formulate the problems of the paper. Later, we present in detail our proposal GCN-M, followed by the experiments on real-life datasets and the conclusion.
Experiments In this section, we demonstrate the effectiveness of GCN-M 1  with real-life traffic datasets. The experiments were designed to answer the following research questions (RQs): RQ 1 Performance on raw benchmark datasets: How well does our model perform on traffic datasets with a few missing values or without? RQ 2 Complex scenarios of missing values: How successful is our model at forecasting traffic data considering the complex missing values scenarios? RQ 3 Dynamic graph modeling: How does our method perform on dynamic graph modeling considering the missing values? RQ 4 One-step processing VS two-step processing: How will our method perform when adopting distinct missing-value processing strategies?.
Experimental settings [Datasets] We base our experiments on the public traffic datasets: PEMS-BAY and METR-LA released by  Li et al. (2018) , which are widely used in the literature. PEMS-BAY records six months of traffic speed on 325 sensors in the California Bay Area. METR-LA records four months of traffic flow on 207 sensors on the highways of Los Angeles County. Both datasets contain some zero and/or missing values, though PEMS-BAY has been pre-processed by the domain experts from the data provider (Caltrans 2015) to interpolate most of the missing values. Following  Li et al. (2018) , the datasets are split with 70% for training, 10% for validation, and 20% for testing. In order to validate the model in complex scenarios of missing values, we introduce complex missing values in the datasets (see details in Sect.
5. 4). In practice, the model should forecast future values from the input data with missing values. Therefore, in the testing set, we mask out the observations from the input sequence X (i. e. , inject missing values) but maintain the complete information for the target Y. We use recent τ = 12 timestamps as input to predict the next T p timestamp. Considering that the missing values are marked as zeros, we scale the input by dividing it with the max speed of the training set instead of applying Z-score normalization. This avoids changing the zero values and facilitates the computation process. Table  2  shows the summary statistics of the datasets. [Evaluation metrics] The forecasting accuracy of all tested models is evaluated by three metrics: mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE).
M AE(Y, Ŷ) = 1 N T p N n=1 T p t=1 | Ŷn t -Y n t | R M S E(Y, Ŷ) = 1 N T p N n=1 T p t=1 | Ŷn t -Y n t | 2 M AP E(Y, Ŷ) = 1 N T p N n=1 T p t=1 | Ŷn t -Y n t Y n t | (22) and N denotes the node numbers, T p represents the forecasting steps. When evaluating each model's performance on the testing set, we mask out the inherent zero values in the prediction targets when computing the metrics. We conduct statistical tests to assess the statistical significance of the differences between the models. In order to compare the forecasters over multiple datasets  (Ismail Fawaz et al. 2019) , we adopted the critical difference diagrams recommended by  Demšar (2006)  and used the Friedman test  (Friedman 1940)  to reject the null hypothesis (i. e. , check whether there are significant differences at all).
[Execution and Parameter Settings] The proposed model is implemented by PyTorch 1. 6. 0 and is trained using the Adam optimizer with a learning rate of 0. 001. All the models are tested on a single Tesla V100 GPU of 32 Go memory. In the multiscale memory module, L, S are set to 12 and 5. n h , n d , n w are all set to 2. We apply four ST blocks in which the Temporal Convolution module contains two dilated layers with dilation factor d ∈ [1, 2]. The embedding dimension d is set to 32.
RQ 1: performance on raw benchmark datasets Recently, a lot of traffic forecasting models (Jiang and Luo 2022) have been proposed, achieving remarkable performance on the benchmark datasets PEMS-BAY and METR-LA. Our objective is not to beat all the models in terms of forecasting accuracy, but to validate our proposal for jointly modeling missing values and forecasting. Therefore, it's essential to know how GCN-M performs in a primary setting, i. e. , on the original datasets with a few missing values or without. We pick three classic Tables  3  and 4  show the performance comparison on the raw PEMS-BAY and METR-LA datasets, respectively. It should be noted that the original datasets already contain missing values (0. 0031% missed in PEMS-BAY, 8. 11% missed in METR-LA). We train the models for single-step (horizon=1) and multi-step (horizon =3,6,12) forecasting.
RQ 2: complex scenarios of missing values In this section, we demonstrate the power of GCN-M in handling complex scenarios of missing values for the purpose of traffic forecasting. As mentioned previously in Fig. 1 , there are several scenarios of missing values in real-life traffic datasets (e. g. , METR-LA): short-range or long-range missing; partial or entire network missing. The results in Tables  3  and 4  did not show the superiority of GCN-M over other models on the original datasets with a low missing rate. To test the model's capability of handling complex missing values, we have designed three scenarios with various missing rates (10%, 20%, and 40%), and removed the observations from the datasets accordingly. We use xi ∈ R n× f ×t to represent each of the observation tensors to be removed from X ∈ R N ×F×τ , therefore a local mask tensor mi ∈ R n× f ×t can be defined accordingly.
This annotates the locations of missing values in the original dataset. All the local mask tensors constitute the global mask sequence M = { mi } which allows injecting missing values with a given missing rate. Then, we designed the scenarios of: • Short-range missing: we randomly set n ∈ [1,. , N ], f = F, t = 1 • Long-range missing: we randomly set n ∈ [1,. , N ], f = F, t = τ • Mix-range missing: we randomly set n ∈ [1,. , N ], f = F, t ∈ [1,. , τ ] In Tables  5  and 6 , we show the performance comparison on the PEMS-BAY and METR-LA datasets under various missing value scenarios. We highlight the best results among the one-step processing models (underlined values) and all the models (bold values). Globally, GCN-M shows the best performance under all the settings when compared with other one-step processing models.
3. 83 The results show a one-hour (12-step) average of the forecasting errors. The underlined values represent the best results among the one-step processing models, the bold values represent the best results among all the models 123  In Fig. 7 , we show the critical difference diagrams  (Demšar 2006 ) on PEMS-BAY and METR-LA datasets under the mix-range missing scenario. A thick horizontal line shows a group of models that are not significantly different in terms of different evaluation metrics. We can also observe that GCN-M is significantly different from other model groups on all the evaluation metrics, which validates the model's performance for processing missing values under complex scenarios. In Fig. 8 , we show the effects of the memory module's parameters L and S on the model's performance.
The two parameters represent the searching range of the local temporal and spatial features, respectively. We report the model's evaluation errors with various missing rates. From the results in Fig. 8 , we observe that when the searching range becomes more extensive, the model's performance decreases more. This can be explained by the mean value of a larger space and the less recent observations will lead to a weaker information dependency with the current timestamp, thus affecting the information enrichment of the traffic embedding. In real-life datasets, we can set the parameters from a small value, such as considering local features during the last one hour (L=12) with five nearest sensor nodes (S=5).
2021 ) are adopted to construct dynamic graphs. • GCN-M-adp: instead of learning dynamic graphs and applying dynamic convolution, an adaptive static graph  (Wu et al. 2019 ) is learned to do the graph convolution. • GCN-M-pre: instead of learning graphs from the traffic embedding or observations, the predefined graphs  (Li et al. 2018 ) calculated with the directed distances between traffic nodes are adopted for doing graph convolution. • GCN-M-com: combine both predefined and learned static graphs  (Wu et al. 2019)  to do the graph convolution. We show in Table  7  the performance comparison on various model variants of the spatial graph modeling. We report the model errors on multiple horizons. We consider the complex scenario of mix-range missing values with a missing rate of 40% on both PEMS-BAY and METR-LA datasets.
2020) , weather and air pollution forecasting  (Han et al. 2021a; El Hafyani et al. 2022; Abboud et al. 2021) , etc. The Spatio-temporal patterns in those data and the missing values caused by the sensor issues or control center errors form similar research problems to this paper. However, GCN-M does have a limitation in terms of computational efficiency. Table  9  shows the per epoch training time comparison on the full datasets between GCN-M and the baseline models. The one-step processing baseline models are much more efficient than other models. This is basically because of their simple structure without integrating the costly graph convolution modules. GCN-M still performs better than DCRNN, but worse than other forecasting models. This is mainly caused by two factors: 1) generating the enriched traffic embeddings requires a huge computation cost on the attention score's calculation in the memory module; 2) generating the dynamic graphs for graph convolution requires learning a large number of parameters, thus increasing computation cost.
Conclusion In this paper, we propose GCN-M, a graph convolutional network-based model for handling complex missing values in traffic forecasting. We studied the complex scenario where missing traffic values occur on both short & long ranges and on partial & entire transportation networks. The enriched traffic embeddings learned by a Spatiotemporal memory module allow handling the complex missing values and constructing dynamic traffic graphs to improve the model's performance. A joint model optimization is applied to consider missing values and traffic forecasting in one-step processing. We compare GCN-M with the one-step processing models, which are specifically designed for processing incomplete traffic data and the recent advanced traffic forecasting models. The extensive experiments on two benchmark traffic datasets with 12 baselines demonstrate that GCN-M shows a clear advantage under various scenarios of complex missing values, as compared to the advanced traffic forecasting models, while at the same time maintaining comparable performance on complete traffic datasets.
experiments on real-life data: The experiments are carried out on two real-life traffic datasets. We provide detailed evaluations with 12 baselines, which show the effectiveness of GCN-M over state-of-the-art. • Joint model optimization: We jointly model the Spatio-temporal patterns and missing values in one-step processing, which allows processing missing values 123 specifically for traffic forecasting tasks, thus bringing better model performance than two-step processing. • Extensive.
49 5. 02 10. 72 4. 99 10. 75 13. 85 24. 62 5. 62 10. 94 5. 17 10. 89 4. 88 13. 01 3. 64 8. 25 Horizon = 6 (30 mins) Horizon = 6 (30 mins) MAE RMSE MAE RMSE 1. 74 3. 97 3. 15 6. 45 1. 85 3. 82 3. 60 6. 51 1. 65 3. 75 3. 06 6. 14 1. 65 3. 75 3. 05 6. 16 1. 69 3. 87 3. 22 6. 55 1. 63 3. 74 3. 11 6. 47 2. 27 4. 24 3. 97 6. 74 2. 26 4. 22 3. 97 6. 74 5. 42 9. 26 7. 45 11. 84 2. 57 5. 52 4. 10 6. 85 2. 33 4. 33 4. 08 6. 86 2. 29 4. 91 5. 46 10. 88 1. 62 3. 64 3. 12 6. 18 MAPE (%) MAPE (%) 2. 90 7. 30 3. 16 8. 00 2. 73 6. 87 2. 80 6. 86 2. 94 7. 66 2. 75 7. 11 3. 98 9. 02 3. 99 9. 05 13. 76 24. 62 4. 08 9. 05 3. 95 8. 75 3. 32 9. 89 2. 76 6. 94 Table 3 Performance comparison on raw PEMS-BAY dataset Table 4 Performance comparison on raw METR-LA dataset PEMS-BAY Horizon = 1 (5 mins) Horizon = 3 (15 mins) METR-LA Horizon = 1 (5 mins) Horizon = 3 (15 mins) Models MAE RMSE MAPE (%) MAE RMSE Models MAE RMSE MAPE (%) MAE RMSE DCRNN 0.
WASP-189 Simbad (1)   HD 133112 HR 5599 TIC 157910432 Gaia DR2 6339097679918871168 V-band magnitude 6. 618 ± 0. 011 Simbad (1) Gaia G-band magnitude 6. 5537 ± 0. 0004 Gaia archive (2)   T eff (K) 8000 ± 80 Spectroscopy M (M ) 2. 031 Notes. The methods used to obtain the stellar parameters are described in the text. The stellar luminosity L is computed following the Stefan-Boltzmann law and using the Stefan-Boltzmann constant σ SB. (1) SIMBAD astronomical database from the Centre de Données astronomiques de Strasbourg (http://simbad. u-strasbg. fr/simbad/). (2) Archive of the Gaia mission of the European Space Agency (https://gea. esac. esa. int/archive/) The CHEOPS phase-curve observations reported in this work furthermore reveal a photometric variability attributed to WASP-189 (see Sect. 4.
The time spent accumulating photons for each frame (a single frame is here the result of 7 stacked images) is referred to as the integration time and is the same for all observations: t int = 33. 6 s. We note that the image read-out is performed in parallel of the next exposure leading to an effective data cadence equal to the integration time (duty cycle of 100%). (1) Each file key refers to a unique observation in the CHEOPS database. [ • C] CH_PR100041_TG000201_V0200 (2) UTC start and end are the starting and ending dates of the observation in UTC. (3) The efficiency represents the ratio between the observation time without interruptions (due to Earth occultation or SAA crossings) and the total observation duration. G RP (Gaia Collaboration 2021a); 2MASS passbands J, H, and K  (Skrutskie et al.
In order to save the downlink bandwidth, observations acquired during SAA crossings are not transferred to the ground. Therefore, CHEOPS' photometric light curves feature gaps on a nearly periodic basis due to these two expected phenomena with typical durations per CHEOPS orbit ranging from 25 to 40 min for the Earth occultation and from 0 to 18 min for the SAA. Each of the CHEOPS observations can be referred to using a file key that is a unique identifier in the mission database. The observations log is shown in Table  2  and lists the file keys of the data sets analysed in this work together with additional information. The earliest data acquired with CHEOPS are the eclipse light curves. The images were taken at the very beginning of the mission, even before the start of the nominal science mission, as part of the Early Science Programme (ESP), which is dedicated to demonstrating the photometric capabilities of CHEOPS.
PSF photometry In addition to the aperture photometry provided by the CHEOPS DRP, we cross-checked our analysis and the results against another photometric extraction technique, PSF photometry. This method consists of fitting the images with a two-dimensional template PSF in order to determine the amplitude of the signal and derive the flux. We used a software package called PIPE, developed specifically for CHEOPS. The code is available on GitHub 2  and will also be extensively described in  Brandeker et al. (in prep. ). PIPE first derives a PSF template library from the observed sub-arrays and imagettes, using a principal component analysis (PCA). The first five principal components (PCs) together with a constant background are then used to best fit the PSF of each image using a least-squares minimisation.
The base assumption of various equations of the code is that the gravitational potential follows a Roche model, which is equivalent to assuming that only the outer layers of the star are distorted by rotation, meaning that the inner layers are spherical, hence producing the same gravitational potential as if the whole mass were concentrated at the centre of the star. pytransit represents the star as a discretised oblate sphere and computes the transit luminosity dip with a discretised planetary disc crossing and partially hiding the stellar object projected onto the plane of the sky. The effective surface gravity g eff at each point on the stellar surface is evaluated from the Newtonian gravity force and the centrifugal force: --→ g eff (ϑ) = - G M r 2 ϑ - → u r + 2π P 2 R sin(ϑ) - → u x , (5) where G is the universal gravitational constant, M is the stellar mass, r ϑ is the distance from the stellar centre of the point considered, P is the rotation period of the star, R is the stellar radius, and ϑ is the colatitude of the point.
Stellar variability In addition to the instrumental systematics and the planet-related signal, the photometric time series feature another flux variability that we studied carefully before including a model for it. Figure  5  shows the raw flux variations after the removal of outliers (see Sect. with a full width at half maximum of 0. 43 days, which is consistent with the stellar rotation period of 1. 24 days computed based on  Lendl et al. (2020). We computed the spectral window of the observations to see how the sampling and gaps in the data impact the periodogram. The spectral window shows peaks corresponding to the orbital period of CHEOPS and its harmonics. This means these peaks are induced jointly by the photometric modulation with the roll angle of the spacecraft and the observation gaps due to Earth occultation and SAA crossings.
The value we obtain by fitting CHEOPS data is much larger than this estimation, and it can be explained by the fact that large amplitudes A ell create a signal in the second phase curve that mimics the variability modelled by the GP in the first phase curve. The global model has a higher likelihood given the ability of the GP to correct for this induced signal, even though its amplitude is not realistic. We thus decide to fix A ell to zero to prevent this effect from biasing the results of the final analyses. The amplitude of the Doppler beaming is consistent with zero when left free, so we remove a degree of freedom by fixing it to zero as well. We validate this choice by computing the expected amplitude following  Esteves et al. (2013) : A beam ≈ α beam K/c ≈ 1 ppm, where α beam is of the order of unity, K is the radial-velocity semi-amplitude, and c is the speed of light.
Phase-curve constraints on the planetary atmosphere The light curve obtained after removing systematic noise and stellar activity assuming a Lambertian reflector and a sinusoidal thermal emission is shown in Fig. 7. The phase curve highlights the exquisite photometric precision of CHEOPS while the planetary eclipse and the asymmetric transit are visible. Figure  8  shows the corrected phase-folded data around the mid-transit time and clearly reveals the effect of GD on the transit shape. The uncertainty on transit and eclipse parameters is small compared to the uncertainty found on the phase-curve signal. This is due to the photometric variability attributed to the stellar activity that is not perfectly periodic and thus hard to disentangle unambiguously from the planetary signal. As described in Sect.