dataesr/scientific-paragraphs-categorization huggingface dataset:
3 , we can clearly see that STRASS has achieved better results than other methods, it is because the haze in image 51 is relatively uniform, so the white noise of this image is not amplified, while the nonuniform haze in other images causes the appearance of white noise. So, the STRASS method is effective, and the contrast is enhanced qualitatively in the way of our visual system. In order to further test the robustness and computing capabilities, we not only conducted comparative experiments on the NTIRE2020 dataset, but also on the images of NTIRE2019. Fig. 4  shows five dense haze images from the NTIRE2019 and the processing results on the state-of-art methods. By observing the first four images, we find that all methods cannot completely remove the haze, especially in areas with thick haze.
Qualitative Evaluation on Dataset In order to evaluate these methods more accurately and compare the slight differences between various dehazed images, we utilize some quantitative evaluation indicators on both NTIRE2020 and NTIRE2019 as Tarel et al. [8]  and ours from NTIRE2020 these are very difficult dehazing situations. Besides the widely adopted structural similarity indices (SSIM  [42] ), we also use PSNR as another important index, the average value of the experiment will be reported later. PSNR and SSIM are the most common and widely used full-reference image objective evaluation indicators. PSNR is an error-sensitive image quality evaluation. SSIM measures image similarity from three aspects: brightness, contrast, and structure. Using these two indicators in the field of image dehazing can well measure the difference between the dehazed image and the label image.
It should be noted that the efficiency of dehazing algorithms can be sensitive to the density of the haze and other obscure parameters that are not necessarily related to the physical parameters of the haze. Our method has been successfully tested on many types of dry or wet particles, such as smoke, fog, dust, mist, haze as the results of the images from the databases of NTIRE2019 and NTIRE2020 attest. For the quantitative assessment, we only considered the image datasets from NTIRE2020 and NTIRE2019 since they represent images that traditional algorithms not based on learning methods cannot easily resolve. Each image database includes 45 pairs of images taken under artificial fog and its corresponding haze-free. The number of image pairs considered for this evaluation is 90 images once the two databases have been merged.
Therefore, we mainly consider comparative experiments with some traditional methods. The algorithm runs directly without a large number of samples, which saves a lot of time for model training, and the average running time on the NTIRE2019 and NTIRE2020 dataset as show in the Table  3. We take the influence of the human visual system (HVS) into account in image dehazing principles, making it easier for humans to understand. These state-of-the-arts artificial intelligence algorithms cannot be perceived by the HVS, so our algorithm is more superior for practical applications. On the other hand, we are also exploring the use of deep learning for dehazing, some works found that traditional algorithms are not as accurate as those based on deep learning. Therefore, this article does not compare experiments with methods based on deep learning.
Analysis and Discussions We discovered the fact that the results of  [5]  are visually quite close to our results on all datasets (without considering the constraint for singular cases), then we can make an assumption that  [5]  may has some of the same properties as STRASS. This could explain why the  [5]  algorithm as well as ours have more white noise on the NTIRE2020 data set, while both of them have shown high visibility on the NTIRE2019 dataset. The proposed method works quite well in general, and can even handle cases of dense and nonhomogeneous haze with the introduction of the constant K (see the Eq. (  12 )). In those cases, our method is able to yield comparable and even better results with advanced non-learning methods. However, a limitation of our method is that it will cause the STRASS to produce white noise when the haze is inhomogeneous.
Funding Statement: This work was supported in part by  National Natural Science Foundation of China  under Grant  62076199 , in part by the  Open Research Fund of Beijing Key   Laboratory of Big Data Technology for Food Safety  under Grant  BTBD-2020KF08 ,  Beijing Technology and Business University , in part by the  China Postdoctoral Science Foundation  under Grant  2019M653784 , in part by  Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences  under Grant  LSIT201801D , in part by the  Key R&D Project of Shaan'xi Province  under Grant  2021GY-027.
2 ). All compounds will be comprehensively characterised by defining their selectivity, potency, and cellular activity. The compounds will be annotated with activity data from a series of established and novel biochemical and cell-based assays, including many derived from primary patient cells. The development of new technologies to discover and characterise chemogenomics compounds and chemical probes is a core component of the project. EUbOPEN partners include scientists from academia and industry developing openly available research reagents and exploring new protein target classes. Of key importance, the EUbOPEN project has a strong commitment to open data and open science, which will accelerate the dissemination of knowledge and project outputs, including chemogenomic library sets, chemical probes, assay protocols and associated research.
To boost the project, EUbOPEN partners interact extensively with other large consortia (e. g. ReSOLUTE,  16  Illuminating the Druggable Genome, 24 Open Targets 25 ) thus minimising the duplication of effort and maximizing global coordination. Finally, sustainability of the project's resources will be ensured through partnerships with chemical vendors and cheminformatics/database providers, which will ensure the long-term availability of the new chemical tool sets, all associated profiling data and established protocols. By working openly, and engaging the wider research community, this project will deliver pharmacological tool compounds for previously understudied targets, contributing to the Target 2035 campaign, and form the foundation for future efforts to generate chemical modulators for the entire druggable genome.
Open chemistry networks projects The creation of such a large number of chemical modulators will require a significant amount of synthetic medicinal and organic chemistry to design, make and test compounds that are not commercially available. The SGC's new Open Chemistry Networks initiative aims to create opportunities for the community to drive the development of these novel probes through a highly distributed, open chemistry network in which chemical resources (compounds, ideas, laboratory space and time) are contributed on a patent-free, open access basis in return for biological evaluation of those compounds by the Target 2035 network, as well as exposure to the educational opportunities afforded by Target 2035. All crowdsourced chemistry inputs and Target 2035-generated assay data will be openly available to the research community without restrictions, under a novel set of open terms, analogous to a Creative Commons Attribution 4.
EU-OPENSCREEN 31 EU-OPENSCREEN  31  is an open screening platform that offers access to a broad range of technologies and tools for the systematic screening of chemical inhibitors. Scientists from academia and industry can implement their screening projects at EU-OPENSCREEN's partner laboratories using the European Chemical Biology Library (ECBL), a diversity compound collection of 140 000 commercially available as well as academic compounds, crowdsourced through chemical biology networks. All generated data and tool compounds are made available in an open access database for use by the wider life science as well as drug discovery communities.
FAIRplus data sharing As outlined above, Target 2035 aims can only be reached with mass collaboration, including openness and data sharing. Given the vast number of different types of biological data sets produced during the chemical probe development process, and differences in data management between institutions, data outputs can be highly variable. The FAIRplus project aims to change and sustain the data management culture by providing tools to ensure that data produced follows community standards and ontologies, along with well-defined data dissemination pathways. As an example, EUbOPEN's mandate is to share its project outputs in a sustainable manner. Therefore, the project is committed to ensuring that all outputs are compliant with FAIR principles: 32 data is findable, accessible, interoperable, and reusable.
The Chemical Probes Portal The Chemical Probes Portal (Portal; https://www. chemicalprobes. org) is a long-standing resource for the biomedical research community that provides expert-curated information on the quality of chemical probes, and enables users to identify the best probes for a biological experiment or study. 5 The Portal was very recently re-launched to provide the community with an enhanced and extended user-friendly interface and integrate chemical compound information from multiple relevant databases to enable user decision-making in terms of the right chemical modulator for a given target and a given biological context. Although there are several complementary online resources available that automatically score the quality of probes using computational approaches,  [33] [34] [35] [36] [37] [38]  such as Probe Miner,  39  thus far the Portal is the only expert-curated resource.
50, 54 The events of the past year and a half have demonstrated how scientists in academia and industry can work together around the world at a pace and scale to address important biomedical problems with open science driving the early stages of drug/vaccine research. Target 2035 aspires to replicate this success but on a human-proteome scale, which will require building a large robust scientific community that works together using open science principles to achieve its ambitious goals and to avoid redundancies and wasting of scarce resources. We have already made significant progress in developing the underlying infrastructure, e. g. , with respect to data handling and mining. Future plans include expansion of the Target 2035 webpage to become a gateway for information sharing and communication among interested scientists.
Acknowledgements The SGC is a registered charity (number  1097737 ) that receives funds from  Bayer Pharma AG ,  Boehringer Ingelheim ,  Canada Foundation for Innovation ,  Eshelman Institute for Innovation ,  Genentech ,  Genome Canada  through  Ontario Genomics Institute  [ OGI-196 ],  EU/EFPIA/OICR/McGill/KTH/ Diamond Innovative Medicines Initiative 2 Joint Undertaking  (EUbOPEN grant number  875510 ),  Janssen ,  Merck KGaA  (aka EMD in  Canada and US) ,  Pfizer ,  Takeda. A. A. A. is primarily supported by a  Wellcome Trust   Sir Henry Wellcome Postdoctoral Fellowship  ( 204735/Z/16/Z ); the  People Programme  ( Marie Curie Actions ) of the seventh  Framework Program  of the  European Union  ( FP7/2007-2013)  under  REA  grant agreement no. 600388  ( TECNIOspring program ); and the  Agency for Business Competitiveness  of the  Government of Catalonia, ACCIO.
M. K. is partly supported by the  National Cancer Institute, USA (NCI)   T32 CA236754. A. C. laboratory receives funding from the  Innovative Medicines Initiative  2 (IMI2)  Joint Undertaking  under grant agreement No  875510  ( EUbOPEN  project). A. R. L. acknowledges support from the  Member States of the European Molecular Biology Laboratory  (EMBL).
Collisions with the left hand/arm resulted in heavy penalization of points. The main task performance (between the EMB and no-EMB conditions) was compared by the reaction time, while the secondary left-hand task performance was quantified by the number of left-hand collisions, and the distance between the hand and the ball, when the ca-button was pressed (see "Methods" for more details). Note that the tasks on both arms involved only finger presses and did not require any hand or arm movement by the participants in either condition. We evaluated the cognitive sense of ownership, agency, location, and task performance using a questionnaire at the end of each condition. Figure  2  shows the answers to the questionnaire, and the proprioceptive drift observed in the two conditions. The source data underlying Fig.
The black circles show the mean and the colored circle with the dot shows the median scores across participants. The box edges show the 25th and 75th percentile of the data and the whiskers show the data range. The questions verified their subjective perception of ownership, location, the agency of the robot arm, their single-and dual-task performance, and the anxiety during their performance. We considered the average score from their first six questions as the measure of embodiment (the embodiment score) perceived towards the robot hand. We also measured the proprioceptive drift in each condition. 1-3 s) and hence the reaction times of right-hand button presses may have been modulated by its temporal proximity to a cabutton press by the left hand. To evaluate this possibility, we assimilated the reaction times from the right-hand button presses into time bins aligned with the ca-button presses (see Fig.
5a ). Specifically, we collected the reaction times observed for trials within five time bins-those recorded before 1. 5 s of a ca-button press (marked as [<-1500]); those recorded between 1. 5 and 0. 5 s before a ca-button press ([-1500, -500]); those recorded between -0. 5 and +0. 5 s of a ca-button press ([-500, 500]); those recorded between 1. 5 and 0. 5 s after a ca-button press ([500, 1500]); and those recorded after 1. 5 s (and before 1. 5 s of the next ca-button press) of a ca-button press ([>1500]). Some data groups were observed to be not normal. Hence, we used an aligned rank transform  19  before performing a two-way repeated measures ANOVA on the reaction times (with the five time bins as factors) and conditions (with EMB and non-EMB conditions as factors). The two-way ANOVA showed a significant effect of time bin (F(4,92) = 20.
Methods Participants. In total, 30 participants took part in this study (mean age = 25. 5, SD = 3. 92, 23 males). Of these, six subjects who were first-time VR users were removed after we observed that first-time users were distracted and concentrated more on the left robot and task, rather than the main right-hand task. The study was approved by the local ethics committee at the University of Montpellier, France as well as the ethics committee at Waseda University, Tokyo (13 participants were appended with additional experiments in Tokyo). We used a student email mailing list to advertise the requirement of participants, and the participants were chosen on a first come first serve basis. The experiment was in Virtual reality and the data was recorded directly by the computer during the experiments.
Any presses when the ball was beyond 30 cm earned them no points. This scenario enabled us to quantify the secondary task performance by the distance between the hand and the ball when the ca-button was pressed, and the collisions incurred by the participants. The dual-task phase consisted of three 2-min trials. The participants performed the above-mentioned tasks with their two hands in every trial. The dual task was the same in both the EMB and no-EMB conditions. We utilized the data from all trials except the first 30 s data of the first trial, in which the performance was assumed to not have stabilized. Finally, at the end of each condition, each participant answered the following 12 questions on a seven-point Likert scale. From 1 (not at all) to 7 (very strongly), it seems like… 1. The robot arm is part of your body 2.
Acknowledgements The authors acknowledge financial support from the  French National Research Agency  ( ANR-16-CE18-0022 ). CFGCG thanks the  Portuguese Foundation for Science and Technology (FCT)/ MCTES  for funding the  Coimbra Chemistry Centre  through the programmes  UIDB/00313/2020  and  UIDP/00313/2020 , also cofunded by  FEDER/COMPETE   2020-EU.
Different machine learning approaches have been successfully applied to the first task under the label 'supertagging'  (Bangalore & Joshi 2011). As in many other research areas, deep learning has greatly improved the accuracy on this task. For example, for the French TLGbank  (Moot 2015) , a maximum-entropy supertagger obtains 88. 8% of the assigned formulas correct whereas the current state-of-the-art using neural networks is at 95. 9%  (Kogkalidis & Moortgat 2022). The second task, linking the atomic formulas, has only recently been implemented using a neural network  (Kogkalidis et al. 2020b ). This provides a complete neural network solution for parsing arbitrary text using type-logical grammars: neural proof nets. Neural proof nets have been successfully applied to the Dutch AEthel dataset  (Kogkalidis, Moortgat & Moot 2020a) , and the French TLGbank  (De Pourtales et al.
It amounts to choosing A = n, B = C = D = np, E = pp and F = G = H = s, which produces the following lexical assignments. Compared to the graph generation task, the labelling tasks in this section are relatively simple: our vertex labelling needs to decide for the first task that "fils. " is a noun n and "le salut. " is a noun phrase etc. , but also, for the second task, that "fils. " is to the immediate right of "Aux", and "le salut" to the immediate right of "apporte". These seem to be tasks where standard graph architectures for vertex labelling would work well  (Wu et al. 2022, Chapter 4). Aux = n ⊸ (pp ⊸ s) ⊸ s fils. = n clame = np ⊸ s ⊸ s -t-il = np j' = np apporte = np ⊸ pp ⊸ np ⊸ F le salut. = np Generating the labels allows for a direct comparison with the state-of-the-art in supertagging for the same dataset  (Kogkalidis & Moortgat 2022 , De Pourtales et al.
On the other hand, the product is extremely rare in our dataset  (Moot 2015 ) and it appears all occurrences A ⊗ B can be replaced by (A ⊸ (B ⊸ s)) ⊸ s, which is both a standard way to implement products as implications in the lambda calculus, and the standard treatment of argument cluster coordination in type-logical grammars. Given the weakness at treating product types, the skeptic would be right to ask whether we're not simply doing natural deduction proof search, thereby losing some of the most important benefits of using proof nets. I think that in a sense, proof nets -especially in the version presented hereare natural deduction proofs, but the product elimination rule is tricky in combination with the graph expansion approach advocated here. Taken together, I think the benefits largely outweigh the drawbacks.
6 The collection of these metadata allowed us to distinguish three groups of participants: i) native, ii) early and iii) late signers. As already mentioned, native signers were individuals exposed to sign language from birth (AoE= 0), having deaf signing parents, and who therefore acquired SL in a familiar environment. Early learners were exposed to sign language between 1 and 5 years of age while late learners between 6 to 15 years of age. In both groups of non-natives, most participants were introduced to sign language in school, almost none had deaf parents, and very few had at least one parent knowing sign language. The following chart in Table  2  summarizes the characteristics of all participants that we considered for the first participant selection. The questionnaires participants filled in were also used to collect more general personal information.
Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in modeling graph structured data and many variants of GNN architectures have been proposed. However, much human effort is often needed to tune the architecture depending on different datasets. Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search. To understand GNN practitioners' automated solutions, we organized AutoGraph Challenge at KDD Cup 2020, emphasizing automated graph neural networks for node classification. We received top solutions, especially from industrial technology companies like Meituan, Alibaba, and Twitter, which are already open sourced on GitHub.
INTRODUCTION Graph structured data has been prominent in our lives and various tasks are studied based upon, including a recommendation on Social Networks  (Fan et al. , 2019) , traffic forecasting on road networks  (Li et al. , 2018) , drug discovery on molecule graphs  (Torng and Altman, 2019) , and link prediction on the knowledge graph  (Zhang et al. , 2020). Graph Neural Networks (GNN)  (Kipf and Welling, 2017)  have been proved to be effective in modeling graph data and tremendous GNN architectures are proposed every year  (Hamilton et al. , 2017; Veličković et al. , 2018; Wu et al. , 2019; Xu et al. , 2019). When applying GNN on graph structured data, expertise and domain knowledge are often required and numerous human effort is needed to adapt to new datasets. Automated Machine Learning (AutoML)  (Yao et al.
, 2018; Hutter et al. , 2019)  aims to reduce human efforts in deploying various applications. AutoML, especially Neural Architecture Search (NAS), has been successfully explored in tremendous applications, including Image Classification  (Tan and Le, 2019) , Object Detection  (Tan et al. , 2020) , Semantic Segmentation  (Nekrasov et al. , 2019) , Language Modeling  (Jiang et al. , 2019) , and Time Series Forecasting  (Chen et al. , 2021). As a result, researchers have started to explore Automated Graph Neural Networks  (AutoGraph). AutoGraph researchers focus mainly on automatically designing GNN architectures by NAS. The majority of these methods focus on designing the aggregation functions/layers in GNNs with different search algorithms  (Zhou et al. , 2019; Gao et al. , 2020; Peng et al.
Toward this end, we organized the first AutoGraph challenge at KDD Cup 2020 and collaborated with 4Paradigm, ChaLearn, and Stanford University. This challenge asks participants to provide AutoGraph solutions for the node classification task. The code is executed by the platform on various graph datasets without any human intervention. Through the AutoGraph challenge, we wish to push forward the limit of AutoGraph as well as to understand the gap between industrial solutions and academic ones. In this article, we first introduce the AutoGraph challenge setting. Then, we present the winning solutions which are open sourced for everyone to use. Finally, we experiment further and compare with NAS for GNN methods and quantify empirically the gap with respect to top solutions. We conclude three gaps of AutoGraph between academia and industry: Modeling scope, Effectiveness, and Efficiency.
Protocol The protocol of the AutoGraph challenge is straightforward. Participants should submit a python file containing a Model class with the required t and predict methods. We prepare an ingestion program reading the dataset and instantiate the class and call t and predict method until the prediction finishes or the running time has reached the budget limit. The ingestion program outputs the model's prediction on test data and saves it to shared space. Then, another scoring program reads the prediction and ground truth and outputs evaluation scores. The execution of the program is always on the challenge platform. When developing locally, we provide a script to call model. py file methods directly.