dataesr/scientific-paragraphs-categorization huggingface dataset:
Learning-based methods are therefore very good candidates for these tasks, since priors can be automatically learnt from data. In this paper, we describe a method that is able to produce an entire light field, estimate scene depth and identify occluded regions from just one single image. This way, we can benefit from light field features without requiring a light field capture set-up, e. g. , simulating perspective shift and postcapture digital re-focusing. We propose a lightweight architecture based on  [Evain and Guillemot, 2020 ], a This work has been funded by the EU H2020 Research and Innovation Programme under grant agreement No 694122 (ERC advanced grant CLIM). but enhanced to be able to generate an entire light field and to better handle occlusions using an adversarial approach. The network is trained on pairs of images and learns to perform a forward and backward view synthesis, with two independent branches, thanks to the estimation of two disparity maps.
Checking the consistency of the two independent predictions allows us to identify occluded regions and compute a disparity confidence map. At test time, the network only needs one image to compute the two disparity maps that are then used to identify the occluded regions. This disparity confidence map is used to control the application of an adversarial technique for occlusion handling. We show that the network can be trained on light field data, and that it outperforms reference techniques trained on light field datasets, such as  [Srinivasan et al. , 2017] , in terms of reconstructed light field quality. Now, training on light field data as in  [Srinivasan et al. , 2017]  necessarily restricts the scope of the approach, as this requires a large amount of data that is not easy to capture.
Besides, such monocular approaches are also bound a lot by semantics of the training data, making it hard to train a network that can be usable for a variety of scene geometry and semantics, unless a sufficient number of examples of diverse scenes is present in the training set. Existing light field datasets are in general too limited to meet that requirement. We show that the proposed architecture can be trained on stereo content. This drastically increases the amount of possible training data that can be exploited by our approach. We show that the proposed network produces very plausible and good-quality light fields even when trained from stereo images with large baselines as in the KITTI dataset  ([Geiger et al. , 2012] ), and this way produces light fields with large fields of view. In summary, our contributions are: • A lightweight neural network based on  [Evain and Guillemot, 2020] , extended to be able to generate a full light field from one single view, with occlusion handling relying on both a computed disparity confidence map and an adversarial approach.
Monocular stereo view synthesis Monocular view synthesis refers to the generation of new views from one single image. This problem has been tackled before the emergence of deep learning techniques, however with some limitations, e. g. with extremely similar images  ([Woodford et al. , 2007] ), or with scenes presenting very similar geometry  ([Horry et al. , 1997] ). Solving this difficult problem requires strong scene priors, which can be efficiently learned from data by using deep learning techniques. The easiest set-up for monocular view synthesis is the stereo case: given a pair of images, the aim is to generate one image from the other. Usually, the two cameras are kept in the same set-up, e. g. in terms of relative distance between the cameras throughout the training dataset, and the network implicitly learns these set-up conditions.
The pioneering work in the domain is deep3D (  [Xie et al. , 2016] ) with a network designed to work on a dataset of 3D movies. The approach automatically generates a 3D sequence from a 2D sequence. A soft measure of disparity is estimated from the input image and is used to warp the final prediction. This method produces images of convincing quality. However, the network is bound to one specific resolution, which implies training several networks for various resolutions. Besides, its number of parameters is very high (around 60 million parameters for a 512 × 256 resolution for the KITTI baseline), and the network is not able to accurately process occlusions. Finally, the produced images tend to be blurry. Other approaches, such as  [Evain and Guillemot, 2020] , evolve over the concept by producing the final image by merging a disparity-based warped prediction and a prediction based on direct minimization.
The presented results are good quality, but the overall approach comes with some limitations: it obtains its more significant results on the Flowers dataset, a dataset of images with very strong similarities in scene semantics and geometry. When the dataset gets a bit more complex and diverse, the approach encounters difficulties. Besides, working from the epipolar constraint also means that the approach gains from working with a large number of views with small baselines at training time, which makes the training process very long. More importantly, the method requires a light field dataset that should be large enough, and consistent enough in both semantics and geometry. This kind of dataset is very rare, and the method cannot be efficiently trained on other data than light fields. In contrast, our method is able to obtain very good results on the Flowers dataset, and thanks to its ability to be trained on stereo content, can also produce light fields with more generic and diverse scenes.
While the method in  [Park et al. , 2017]  is very efficient on 3D models, or very simple scenes, it is less efficient on natural images. Besides, it requires ground truth occlusion maps in the training set, which are not easy to capture. This reduces the amount of datasets that can be used for training. The authors in  [Tulsiani et al. , 2018]  present a method able to distinguish the foreground from the background through learning. Even if the results are interesting, they require a significantly heavier architecture than ours in terms of number of parameters. In our case the occluded regions are automatically identified through forward-backward checks, and we use an adversarial loss to generate plausible content in occluded regions. GANs  ([Goodfellow et al. , 2014] ) have been shown to be very useful for inpainting, e.
In this article, we used two datasets for comparison: Flowers ([  Srinivasan et al. , 2017] ) and KITTI  ([Geiger et al. , 2012] ). Flowers is a light field dataset, with rather small baselines, comprising around 3,000 light fields of flowers in similar geometrical configurations. We systematically pick the central view as one element of the pair, and we randomly choose another view as the other element of the pair. We adjust the value of α to account for the coordinate of the selected view. As a starting point, we only focus on one corner view as target that we arbitrarily choose as the reference disparity (α = (1, 1)). After 10 epochs, we add   Srinivasan et al. , 2017] ), and our stereo-based method (Stereo). We display the mean PSNRs and SSIM on the 4 corner views (the most difficult ones to predict), as well as on the full light field.
Light Field View Synthesis Results Training and testing with light field data We first focus on training and testing the network with light fields. For that, we use the Flowers dataset (  [Srinivasan et al. , 2017] ). We evaluate predicted views in comparison with the reference method   Srinivasan et al. , 2017] ) and the stereo version of our method. The errors were multiplied with a factor of 3 for better visualization. Figure  5 : Close-up views from figure 4. On the left side, our results, on the right side, the results obtained in  [Srinivasan et al. , 2017]. We note that our results are sharper and structurally more consistent. LF4D  ([Srinivasan et al. , 2017] ), by applying an identical experimental protocol, in figures 4, 5 and 6, in table 1, as well as in the supplementary video.
We see that our approach clearly outperforms LF4D, both metric-wise and visually. We also use the Flowers dataset to evaluate our stereo-training based approach, i. e. by only training the network on stereo aligned pairs (extreme left-side view -center view and center view -extreme right-side view). The results (the last row of figure  4  and table  1 ) show that our method, even when trained on stereo content, manages to outperform the LF4D monocular light field synthesis method, and is able to produce high-quality light fields. This shows that our stereo to light fields adaptation module is very efficient. Figure  6 : Supplementary visual comparisons between our work (left-side) and  [Srinivasan et al. , 2017]  (right-side). We note that our images are sharper and better quality. Training on stereo content We also train the network using the stereo KITTI dataset  ([Geiger et al.
, 2012] ), in order to build a full light field. The views produced have no ground truth equivalent; only visual evaluation is possible in this case. Visual results are shown in figure  1  and in the supplementary video. To evaluate our approach, we compare it visually to the monocular part of the method in  [Sun et al. , 2018]. The network, also trained on KITTI, receives as input one image and a transformation vector expressing the relative coordinates of the target view. We specify to the pre-trained network a transformation vector similar to ours. We note that our approach clearly performs better visually on this data (see figure  7 ). This is probably because the vertical transformations are not present in the KITTI training set, and can thus not be learnt efficiently by the method in  [Sun et al.
Ablation study Impact of the confidence-based refiner We evaluate the impact of the refiner on the result in tables 3 and 5. We can note that it significantly increases the performance both in PSNR and SSIM for both datasets. Its contribution is, though, more significant when working on KITTI, due to its more significant occluded regions. We also evaluate its positive contribution when training the approach on stereo contents, and using it to generate light fields in table 4. We note that the Refiner in this case also allows to significantly improve the performance of the approach. Impact of adversarial learning We also evaluate the impact of our adversarial process on the result. We note that depending on the chosen dataset, we do not draw the same conclusions. When working on Flowers (see table  3 ), we note that the adversarial process does not really have a significant impact.
A previous systematic search was updated and electronic databases (Ovid MEDLINE(R), Ovid MEDLINE(R) and In-Process & Other Non-Indexed Citations, Embase, PsycINFO, Cochrane Database of Systematic Reviews, and CINAHL) were last searched on October 13, 2020. The risk of bias was assessed using the Cochrane Collaboration's tool for randomized trials. A random-effects meta-analysis for the severity of fatigue across different scales at the end of the intervention was conducted. A total of 1035 participants were randomized across 19 studies. We estimate that less than 10% of the randomized trials of physical activity for CRF include people with post-cancer fatigue. The effect of physical activity on post-cancer fatigue was modest and variable (Hedges's g -0. 40; p = 0. 010; 95% prediction intervals -1.
Search Strategy We updated the search used in a previous systematic review  (Oberoi et al. , 2018)  in collaboration with co-authors of that review (LS, PDR, NCR). In the previous review, the search was conducted from 1980 to May 11, 2017. In order to identify studies indexed since May 11, 2017, we performed a new search and replicated the previous search strategy. We searched for eligible trials from 2016 to October 13, 2020, using identical search terms in the following electronic databases: Ovid MEDLINE(R), Ovid MEDLINE(R) and In-Process & Other Non-Indexed Citations, Embase, PsycINFO, Cochrane Database of Systematic Reviews, and CINAHL. The search strategy is included in our supplementary material.
Data Extraction Search results were stored and de-duplicated in Zotero, an open-source reference management software (https://www. zotero. org/). Following de-duplication, screening of the references retrieved in the literature search was performed in a systematic review software  (Ouzzani et al. , 2016). The titles and abstracts were independently screened for eligibility by two researchers (SY, RT), blinded to the other researcher's decisions during this initial process. Any publication considered potentially eligible by either reviewer was retrieved in full and assessed for eligibility. After this process, the inclusion of studies in this meta-analysis was determined by the agreement of both reviewers. Discrepancies between the two reviewers were resolved by consensus and adjudication by a third reviewer (JGW).
(a) Risk of Bias The risk of bias was assessed using the Cochrane Collaboration's tool for randomized trials (RoB 1. 0,  Higgins et al. , 2011). RT and JGW graded each risk of bias parameter as high risk, low risk, or unclear risk based on recommendations for judging the risk of bias  (Higgins et al. , 2011). The approach evaluates sequence generation and allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessors (outcome bias), incomplete outcome data (attrition bias), selective reporting (reporting bias) and other biases. For attrition bias, we judged studies to be at high risk when more than 30% of data were missing for the post-intervention (short-term) followup. For reporting bias, one reviewer (RT) checked for a major discrepancy between the registered and published outcomes of these trials and decisions were reviewed by a second reviewer (JGW).
(b) Meta-Analysis A meta-analysis for the severity of fatigue across different scales at the end of the intervention was conducted. For two trials that contained three arms (two eligible physical activity groups versus a control group)  (Kröz et al. , 2017; Yuen, 2007) , a single pairwise comparison was created by combining data from both groups using methods recommended in the Cochrane Handbook for Systematic Reviews of Interventions  (Higgins et al. , 2019). For trials with two arms comparing two different types of physical activities, either the aerobic or the activity of lower intensity was chosen as the experimental arm (with the consensus of RT, SY and JGW). Data were analyzed for the first time-point following completion of the intervention to maximize the data available and due to heterogeneity in the timing of follow-up.
Results The flow diagram of study identification and selection is presented in Figure  1. The database search identified 10,044 citations, of which 6301 were removed as duplicates, including 29 citations that were duplicated with  Oberoi et al. (2018)  (due to a planned overlap in search dates). From the 3743 records, we evaluated 67 full texts. Five studies met the eligibility criteria and were included in this systematic review  (Abrahams et al. , 2016; Kim et al. , 2020; Kröz et al. , 2017; Pagola et al. , 2020a; Sandler et al. , 2017). One study required adjudication with a third reviewer  (Pyszora et al. , 2017). That study investigated two weeks of physiotherapy for patients admitted to a palliative care service and was excluded due to the severity of the disease and high risk of deterioration in the general condition of the participants during the study period  (Pyszora et al.
(f) Meta-Analysis Sixteen studies were included in the meta-analyses (data from 758 participants)  (Abrahams et al. , 2019; Bennett et al. , 2007; Bower et al. , 2012; Cantarero-Villanueva et al. , 2013; Donnelly et al. , 2011; Kim et al. , 2020; Kröz et al. , 2017; Larkey et al. , 2015; Mayo et al. , 2014; Pagola et al. , 2020a; Prinsen et al. , 2012; Rogers et al. , 2014; Sandler et al. , 2017; Stan et al. , 2016; Yuen, 2007). Three studies were excluded due to a lack of data or lack of response to a request for additional data. Data from one study  (Heim et al. , 2007)  was extracted from a figure using WebPlotDigitizer  (Rohatgi, n. d. ). Data, code and additional details for the meta-analysis are available in supplements. The reduction in the severity of fatigue in the physical activity intervention compared to control was g = -0.
The functionality of FuzzyQ is shown with two real datasets. We demonstrate how FuzzyQ can effectively be used to monitor and model spatio-temporal changes in species commonness, and assess the impact of species introductions on ecological communities. We also show that the approach works satisfactorily with a wide range of communities varying in species richness, dispersion and abundance currencies. 4. FuzzyQ produces ecological indicators easy to measure and interpret that can give both clear, actionable insights into the nature of ecological communities and provides a powerful way to monitor environmental change on ecosystems. Comparison among communities is greatly facilitated by the fact that the method is relatively independent of the number of sites or sampling units considered. Thus, we consider FuzzyQ as a potentially valuable analytical tool in community ecology and conservation biology.
Overview of FuzzyQ FuzzyQ is provided as an R package (R Core Team, 2020), available at https://github. com/Ligophorus/FuzzyQ, which depends on algorithms implemented in package cluster  (Maechler, Rousseeuw, Struyf, Hubert, & Hornik, 2019). We first illustrate application of FuzzyQ with a dataset of ant species (ants_Darwin_A in  Calatayud el al. , 2019)  comprising the abundance of 46 species in 100, 18×18 m plots sampled in the Northern Territory, Australia  (Arnan et al. , 2011). A matrix of lower and upper bound limits at a given confidence level (default 95%).
AOplot Plots the abundance occupancy relationship of a fuzzyq object. A scatter plot of fraction of sites occupied by each species vs mean abundance per site. A matrix sorted by cluster matriz. Species are arranged by cluster and by increasing silhouette width within cluster Table  1  provides an overview of the functions in package FuzzyQ. Function fuzzyq takes a given site-by-species abundance matrix and performs a fuzzy clustering algorithm that evaluates all pairwise dissimilarities among species in terms of their AORs to allocate each species into two clusters of common and rare species, respectively. Since occupancy and abundance are in different scales and can come in different units (for instance, the former can be reported as either number or fraction of sites occupied), fuzzyq uses by default  Gower's (1971)  dissimilarities, which are appropriate for such mixed data.
Clustering is subsequently performed with function fanny in cluster, which aims to minimize the objective function ∑ 𝑢 𝑢 𝑑 𝑖, 𝑗 , 2 ∑ 𝑢 , (1) where  and  are the membership coefficients of observations (species in our case) i and j to cluster v, n is the number of observations, k is the number of clusters (herein 2: common and rare), a is a membership exponent (we set a = 2 as in the original formulation of  Kaufman & Rousseeuw 1990 ) and d(i, j) is the dissimilarity between observations i and j  (Maechler et al. , 2019). Fig. 1a  displays the fuzzyq allocation of species of the ant dataset to the rare and common clusters. In fuzzy clustering, each observation can be assigned to several clusters with a different level of certainty. So uiv in (1) above represents the probability of the i th observation belonging to cluster v  (Kaufman & Rousseeuw, 1990).
Fig. 1c  shows the species silhouettes of the ant database. Silhouettes can range between -1 and +1. The high positive values of most rare ant species indicate that they are well matched to its own cluster. Common ant species showed smaller silhouette widths, suggesting a weaker cluster. In particular, the negative silhouette of species 27 indicates a poor fit to the common-species group (Fig. 1c ), which conforms to its position in the AOR plot and its Ci ≈ 0. 5 (Fig. 1a, b ). fuzzyq also computes community-level metrics that measure the coherence of the common-and rare-species clusters, and the strength of overall classification. The former is assessed by the average silhouettes' widths of the common and rare species (S̅ C and S̅ R, respectively) and, alternatively, by the corresponding average commonness coefficients (C ̅ C and C ̅ R).
The latter can be appraised by the average silhouette width of the whole community (S̅ ) and the normalized Dunn's partition coefficient (D')  (Kaufman & Rousseeuw, 1990). The Dunn's coefficient is computed as 𝐷 𝑢 /𝑛 , where n is the number of observations (i. e. , species). D is subsequently normalized to vary between 0 (complete fuzziness) and 1 (hard clusters). When k = 2, as in our case, the normalized Dunn's coefficient is ′ 2 1  (Kaufman & Rousseeuw, 1990). Fig. 2  displays the global metrics of the ant species database. S̅ and D' were 0. 79 and 0. 69, which suggests a strong clustering structure separating common and rare species  (Kaufman & Rousseeuw, 1990). We assessed the variation of the global estimates by bootstrapping the sites of the sites × species matrix with fuzzyqBoot. Compared with common ones, rare species showed a higher average silhouette width, and showed lower variation in both silhouettes and commonness indices, indicating that they form a harder cluster (Fig.
Example 1. Mammal Data from Powdermill Biological Station 1979-1999 To illustrate how to monitor changes in species commonness in a community, we used a long-term  time series of small mammal abundances from the Powdermill Biological Station in Pennsylvania, USA  (Merrit, 2013). Mammals were captured in a 1-ha live trapping grid consisting of 10 × 10 quadrats of trap stations at 10-m intervals. Trapping was conducted bimonthly from September 1979 to October 1999. For the sake of demonstration, the abundance of each mammal species was aggregated per quadrat and per year in order to capture the annual variation in commonness of each species and we assumed that the pool of species did not change over the study period (random absences). Year a) b) The Cis indicated that two and seven of the 14 species could be categorized consistently as common and rare, respectively, throughout the study period.
Effect of number of sites A key question for users interested in comparing different communities is whether the community-level estimates depend on the number of sites sampled. We examined this issue using 20 datasets compiled in  Calatayud et al. (2019)  and  (Jeliazkov et al. , 2020)  involving 87+ sites. These included communities varying widely in taxonomic composition, species richness and spatial scale. In addition, different abundance currencies were employed. (See Table  S1  in Supporting Information for details. ) Being N the total number of sites of a given dataset, global metrics (S̅ , S̅ C, S̅ R, C ̅ C, C ̅ R and D') were computed in most cases dropping successively 1, 2, 3, …, N-10 sites randomly drawn (without replacement) from the dataset. (In species-poor communities or communities with very sparsely distributed species, the series was 1, 2, 3, …, N-20).
General Escherichia coli strain BW25113 was obtained from the National BioResource Project, National Institute of Genetics, Japan. The reference genome sequence of this strain was taken as GenBank sequence CP009273. 1. A complete list of strains used in this study is given in Supplementary Table  S1. Oligonucleotides were from Integrated DNA Technologies (Coralville, IA). Oligonucleotide sequences are listed in Supplementary Table  S2. Synthetic DNA fragments were from Twist Bioscience (South San Francisco, CA) or Integrated DNA Technologies. Sanger sequencing was performed by FASMAC (Atsugi, Japan). A single batch of Luria-Bertani (LB) medium (Nacalai-Tesque, Kyoto, Japan) was used for experimental evolution and growth assays.