dataesr/scientific-paragraphs-categorization huggingface dataset:
Integrative modeling of the exosome complex The 10-subunit yeast exosome complex is a macromolecular machine responsible for processing and degrading RNA in eukaryotic cells  (Houseley et al. , 2006). To model this complex, we used the structures of all subunits of the complex in one state, the crystal structure of the RNA-bound exosome (PDB code 4IFD). We also incorporated independent data collected in another state, the low-resolution cryo-EM map of the RNA-free exosome (EMDB code 3367)  (Liu et al. , 2016)  and a dataset of 98 cross-linked residue pairs obtained by XL-MS  (Shi et al. , 2015)  (Fig. 7 , Fig. S7 , and Fig. S8 ). To the best of our knowledge, no high-resolution structure of the RNA-free 10-subunit exosome complex alone is available. We thus used the structure of the RNA-free exosome in complex with Ski7 (PDB code 5G06) as a reference to test the accuracy of our models.
Comparison with existing approaches To compare our approach with state-of-the-art methods for modeling macromolecular complexes using low-and intermediate-resolution cryo-EM maps, we first examined the results of the benchmark carried out with γ-TEMPy  (Pandurangan et al. , 2015). This method scores the models using mutual information between model and experimental densities and uses a genetic algorithm to accelerate sampling. In the following, we compare our approach with γ-TEMPy in terms of accuracy of the scoring function, sampling efficiency, and computational performances. In order to compare the accuracy of the two approaches, we examined our best scoring model (Table  2 ) and the γ-TEMPy high scoring model (HS in Table  1  of Ref. (Pandurangan et al. , 2015) ) on a subset of 9 test cases of our benchmark that were also included in the γ-TEMPy benchmark (PDB codes 2DQJ, 2BO9, 2BBK, 2GC7, 1VCB, 1TYQ, 1MDA, 1GPQ, and 1CS4).
Dissemination We implemented our modeling protocol in a series of scripts based on IMP. pmi  (Webb et al. , 2018) , a module of the Integrative Modeling Platform (IMP, http://integrativemodeling. org) (Russel et al. , 2012)  that can be used to build the system representation, setup the scoring function, define the degrees of freedom to sample, and finally analyze the solutions. Our approach was also implemented in the PLUMED-ISDB module  (Bonomi and Camilloni, 2017)  of the open-source PLUMED library (www. plumed. org) (Tribello et al. , 2014). Thanks to the differentiability of the scoring function, this implementation can be used for real-space, flexible refinement of individual models using molecular dynamics at atomistic resolution or, in combination with metainference  (Bonomi et al.
this situation: p M Ψ D ∝ p M ϕ D ∝ p ϕ D M ⋅ p(M) (5) Divide-and-conquer fit of the data-GMM. -To fit the experimental density map Ψ D with a GMM ϕ D , we used the Expectation Maximization algorithm implemented in the gmconvert software  (Kawabata, 2008). This approach determines the parameters of the GMM (mean, weight, and covariance matrix of each Gaussian component) by maximizing the likelihood that the GMM density function generates the density of the voxels in Ψ D. As the resolution of the map increases, the number of Gaussians required for the GMM to accurately reproduce all the features of the experimental map increases exponentially along with the computational time and memory required to perform the fit. To overcome these challenges, we developed a divide-and-conquer approach (Fig.
2 ). First, the map Ψ D is masked and all voxels with a density lower than the threshold recommended in the EMDB database are removed. Second, a recursive procedure starts from a first iteration in which the map Ψ D (Fig. 2A ) is fit with a GMM consisting of a small number of Gaussians N D. (typically 2 or 4) (Fig. 2B  Each components ϕ D,i of this initial GMM is used to partition the original map into sub-maps Ψ D,i (Fig. 2C ): Ψ D, i (x) =Ψ D (x) • ϕ D, i (x) ∑ j = 1 N D ϕ D, j (x) (6) This partitioning has two properties: a) each sub-map selects the part of the original map that overlaps with the component (ϕ D,i ); b) the sum of all sub-maps results in the original density map: Ψ D (x) = ∑ i = 1 N D Ψ D, i (x) The process is repeated, and each sub-map Ψ D,i is again fitted using a GMM with a small number of Gaussians N D (Fig.
Benchmark of the modeling protocol Data generation. -We benchmarked our modeling protocol using 21 protein/DNA complexes consisting of 2 to 7 subunits  (Velazquez-Muriel et al. , 2012)  (Table  2  and Fig. 4 ). For each of these complexes, we generated a simulated cryo-EM density map, using the structures extracted from the PDB. We used one Gaussian for every 1. 090 kDa of assembly mass, which corresponded approximately to the mass of 10 residues and resulted in a resolution of approximately 10 Å, as obtained by extrapolation from the stretchedexponential regression (Fig. 3B ). For example, the human transferrin receptor complex (PDB code 1SUV)  (Cheng et al. , 2004 ) consists of 6 subunits and has a molecular mass of 290 kDa. Therefore, the simulated map was determined using 262 Gaussians.
The simulated GMMs were generated from the reference structures using the program gmconvert  (Kawabata, 2008). Subunits representation and forward model. -Molecules (protein and DNA chains) were represented by a set of spherical beads, each with the volume of the corresponding residue. When available, the positions of beads were obtained from the PDB structures and constrained into one or more rigid bodies. Missing regions were constructed as strings of flexible coarse-grained beads. When molecules were intertwined or if a molecule was composed of structurally independent domains, we defined several rigid bodies, one for each domain. Furthermore, in some cases, two domains belonging to distinct molecules were merged into the same rigid body, such as the DNA double-strands in 3V6D and 2Y7H or the helical bundle of 3PUV.
The model-GMM was computed as follows. First, for each rigid body defined above, we computed a GMM based on the corresponding atomic coordinates using the implementation of the expectation-maximization algorithm available in the scikitlearn python library  (Pedregosa et al. , 2011). The number of Gaussians of a model-GMM was determined by dividing the molecular weight of the corresponding rigid body by the average weight of a 10-residue peptide (1. 09kDa). The center and covariance matrix rotation of each Gaussian were constrained into the corresponding rigid body. Second, each flexible bead was treated as an individual spherical Gaussian. Model sampling. -The initial positions and orientations of rigid bodies and flexible beads were randomized. The generation of structural models was performed using MC coupled with replica exchange  (Swendsen and Wang, 1986).
If the resulting cross correlations were below 0. 7, we started another iteration run. The threshold of 0. 7 was chosen by experience, as we noticed that lower cross correlation coefficients usually indicate poor agreement of the model with the cryo-EM data with clearly misplaced subunits. This procedure was applied to all cases for which the simple replica exchange above did not yield satisfying results (PDB codes 1MDA, 1SUV, 1TYQ, and 3PUV in the synthetic benchmark and the application to the RNA polymerase II and exosome complexes). Analysis. -All models produced by the modeling protocol described above were ranked by score, and the 1000 best scoring models were considered for further analysis. The accuracy of the fit was assessed by computing a series of structural metrics, namely the rmsd, p(10), the correlation coefficient between the model-and data-GMMs, as well as the average placement score of the best scoring model.
Modeling of the GroEL/ES complex We modeled the architecture of the 21-subunit GroEL/ES ADP-bound complex using cryo-EM data at 23. 5 Å resolution (EMDB code 1046) (Fig. 5 ). The GroEL/ES complex consists of 2 sequences, the chaperonin GroEL and the cochaperonin GroES, with a stoichiometry 14:7. The 14 copies of GroEL have two distinct structures, named GroEL-cis and GroELtrans. The 7 GroES, 7 GroEL-cis, and 7 GroES-trans are arranged in a C7 symmetry, each one occupying one centro-symmetric ring (Fig. 5 ). The optimal data-GMM contained 256 components (Fig. S4A ). We followed the modeling protocol and the model representation used for the benchmark with synthetic cryo-EM maps (Fig. S4B ). The coordinates of the beads used to represent our system were obtained from PDB code 1AON  (Armache et al.
, 2005). Each protein was constrained into a rigid-body based on the crystallographic structure, and a C7 symmetry constraint is applied. The number of residues per coarsegrained bead was set to 20, and the number of residues per component of the model-GMM was set to 10. The quality of the resulting models was assessed using the same structural metrics as in the benchmark with synthetic data, using the structure of PDB code 1AON as reference.
Integrative modeling of the RNA polymerase II complex We modeled the architecture of the 12-subunit RNA polymerase II using cryo-EM data at 20. 9 Å resolution (EMDB code 1883)  (Czeko et al. , 2011)  and two datasets of 108  (Chen et al. , 2010)  and 157  (Robinson et al. , 2015)  cross-links (107 inter-and 158 intra-molecular) (Fig. 6  and Fig. S5 ). The RNA polymerase II complex consists of 12 subunits, named Rpb1 to Rpb12. The optimal data-GMM contained 64 components (Fig. 6A ). We followed the modeling protocol and the model representation used for the benchmark with synthetic cryo-EM maps (Fig. S6 ). The coordinates of the beads used to represent our system were obtained from PDB code 1WCM  (Armache et al. , 2005). Based on a prior domain analysis of Rpb1 and Rpb2, we constrained the coordinates of these two large subunits into four rigid-bodies corresponding to: 1) residues 1141-1274 of Rpb1, 2) residues 1275-1733 of Rpb1, 3) residues 1-1102 of Rpb2, and 4) residues 1-1140 of Rpb1 together with residues 1103-1224 of Rpb2.
The number of residues per coarse-grained bead was set to 20, and the number of residues per component of the model-GMM was set to 10. The quality of the resulting models was assessed using the same structural metrics as in the benchmark with synthetic data, using the structure of PDB code 1WCM as reference. The XL-MS data was encoded using a previously developed Bayesian scoring function  (Shi et al. , 2015).
Integrative modeling of the exosome complex We modeled the architecture of the 10-subunit yeast exosome complex using cryo-EM data at 11. 5 Å resolution (EMDB code 3367)  (Liu et al. , 2016)  and a dataset of 98 cross-links (26 inter-and 72 intra-molecular)  (Shi et al. , 2015)  (Fig. 7  and Fig. S7 ). The exosome complex consists of a core complex of 9 proteins (Csl4, Mtr3, Rrp4, Rrp40, Rrp42, Rrp43, Rrp45, Rrp46, and Ski6), and an RNase protein (Dis3). The top of the core complex recruits RNAs that are then transferred to Dis3 through a central channel in the core complex. The optimal data-GMM contained 784 components (Fig. 7A ). We followed the modeling protocol and the model representation (Fig. S8 ) used for the benchmark with synthetic cryo-EM maps, with few variations. We split the largest subunit, Dis3, into three rigid-bodies, corresponding to residues 1-237, 238-471, and 472-1001, which is the domain organization of this subunit based on its structure in PDB code 4IFD (Fig.
S7 )  (Makino et al. , 2013). The quality of the resulting models was assessed using the same structural metrics as in the benchmark with synthetic data. The only difference was that the reference structure used to compute the accuracy (PDB code 5G06) was different from the structure used to initialize the positions of beads in rigid-bodies (PDB code 4IFD)  (Liu et al. , 2016). As for the modeling of RNA polymerase II, XL-MS data was encoded using a Bayesian scoring function  (Shi et al. , 2015).
(B) Relationship between map resolution and number of components of the data-GMM. For all the density maps of panel A, the experimental resolution is plotted as a function of the optimal number of components of the data-GMM normalized by the molecular weight of the complex (solid circles). The points are fitted using a power law (blue line). The orange and purple circles correspond to maps whose resolution was determined by the Fourier Shell Correlation 0. 143 and 0. 5, respectively. (B, inset) For each density map, the optimal number of components is computed as the minimal absolute relative deviation |Δr|/r between the data-GMM resolution and the density map resolution. We report the same information as in Fig. 6  for the case of the yeast exosome complex, with the following differences: (A) the optimal number of components used in the modeling is 784; (D) the reference structure is taken from PDB code 5G06.
Benchmark of the divide-and-conquer approach for GMM fitting. For each of the system studied, we report: the EMDB accession code, the reference paper, the molecular weight of the complex, the resolution of the map, the method used to quantify the experimental resolution, the optimal number of components, the resolution of the optimal GMM, the CC between the experimental cryo-EM map and the optimal GMM. Results of the benchmark of the modeling protocol.
EMDB For each of the system studied, we report: the PDB accession code, the reference paper, the number of subunits, the number of clusters, the average time needed to produce one model, the rank and rmsd of the model with minimum rmsd with respect to the reference structure (best rmsd model). We also report the following information about the best scoring model: rmsd, p(10), the data-model correlation coefficient (CC), and the average placement scores (APS) with respect to the reference structure.
To address this challenge we suggest to consider multiscale controlled lacunary structures which fill a given geometric hull, with imposed functional surface. A second application is tree-like structures support design for additive manufacturing or for reinforcing a given functional surfaces (see Fig. 1  ). In both cases, imposed geometric inputs, geometric hulls or functional surfaces, come from a CAGD software and are generally represented by Non-Uniform Rational B-Splines (NURBS). From the past decades, NURBS imposed themselves into CAGD. They are a powerful tool to construct smooth surfaces while respecting usual constraints of industrial conception. Our iterative modeler have to interact with CAGD software and needs to integrate NURBS representation. From a theoretical point of view, it could be interesting to have a common representation for fractals, subdivision surfaces and NURBS to identify possible interactions.
Besides, knowledge-aware recommender systems have also the advantage of being able to naturally leverage Linked Open Data  [4] , which provide a rich database of item descriptions and model item-item relations with semantic properties  [10]. In this paper, we describe Tinderbook, a book recommender system based on knowledge graph embeddings that provides book recommendations given a single book that the user likes. To achieve this, we extend a state-of-the-art knowledge graph embeddings algorithm  [15]  to compute item-to-item recommendations using a hybrid item relatedness measure. In Sect. 2, we describe the recommendation algorithm, the dataset and the experimental validation of the methodological choice. In Sect. 3, we provide a high-level description of the Tin-derBook end-user application.
Offline Evaluation The dataset used for the application and for the offline evaluation is Library-Thing 5  , which contains 7,112 users, 37,231 books and 626,000 book ratings ranging from 1 to 10. LibraryThing books have been mapped to their corresponding DBpedia entities  [8]  and we leverage these publicly available mappings to create the knowledge graph K using DBpedia data. As done in previous work  [17] , we select a subset of properties of the DBpedia Ontology 6  to create the knowledge graph: ["dbo:author", "dbo:publisher", "dbo:literaryGenre", "dbo:mediaType", "dbo:subsequentWork", "dbo:previousWork", "dbo:series", "dbo:country", "dbo:language","dbo:coverArtist", "dct:subject"]. We create a 'feedback' edge between a user and a book node when the rating is r ≥ 8, as done in previous work  [8, 17].
For the offline evaluation, we split the data into a training X train , validation X val and test set X test containing, for each user, respectively 70%, 10% and 20% of their ratings. Users with less than 10 ratings are removed from the dataset, as well as books that do not have a corresponding entry in DBpedia. After the mapping and the data splitting, we have 6,789 users (95. 46%), 9,926 books (26. 66%) and 410,199 ratings (65. 53%). We use the evaluation protocol known as AllUnratedItems  [22] , i. e. for each user, we select as possible candidate items all the items present in the training or in the test set that the user has not rated before in the training set: I candidates (u) = I \ {i ∈ X train (u)} (4) We use standard metrics such as precision (P@k) and recall (R@k) to evaluate the ranking quality.
We iterate through the users of the LibraryThing dataset, and for each user we sample with uniform probability an item i j that he/she liked in the training set. Then, we rank the candidate items i k ∈ I candidates (u) using ρ entity2rec (i j , i k ), ρ itemknn (i j , i k ), ρ RDF 2V ec (i j , i k ) and MostPopular, and we measure P@5, R@5, SER@5, NOV@5. The results show that entity2rec obtains better precision, recall and serendipity with respect to competing systems (Table  1 ).
Session A complete usage session can be divided in two phases (Fig. 2 ): 1. Onboarding: the user lands on the application and gets books that are sampled with a chance that is proportional to the popularity of the book. More in detail, a book is sampled according to: p(book) ∼ P + (book) 1 T ( 9 ) where P + is the popularity of the book, which is defined as the fraction of positive feedback (ratings r ≥ 8) obtained by the book in the LibraryThing dataset. T is a parameter called "temperature" that governs the degree of randomness in the sampling. T → 0 generates a rich-gets-richer effects, i. e. most popular books become even more likely to appear in the extraction. On the contrary, when T grows the distribution becomes more uniform, and less popular books can appear more often in the sampling.
Architecture The overall architecture is presented in Fig. 4. DBpedia is the main data source for the application. DBpedia is queried to get book title, author and abstract. Google images is queried to retrieve thumbnails for images, using the book title and author extracted from DBpedia to disambiguate the query. The model is a key-value data structure that stores item-item similarities as defined in Eq. 1 and it is used to get the five most similar books to the chosen book. MongoDB is used to store the discarded books, seed books, and the feedback on the recommended books ("thumbs up" or "thumbs down"), in order to evaluate the application in the online scenario (Sect. 4). Book metadata are collected once for all the books at the start of the server and kept in memory to allow faster recommendations.
Cards serve as entry points to more detailed information. According to Carrie Cousins, cards can contain multiple elements within a design, but each should focus on only one bit of information or content  [6]. A famous example of card-based GUI is that of the dating application Tinder, and according to Babich: "Tinder is a great example of how utilizing discovery mechanism to present the next option has driven the app to emerge as one of the most popular mobile apps. This card-swiping mechanism is curiously addictive, because every single swipe is gathering information -cards connect with users and offer the best possible options based on the made decisions"  [3]. Finally, TinderBook leverages DBpedia  [4]  and this allows to leverage a wealth of multi-language data, such as book descriptions, without the cost of creating and maintaining a proprietary database.
Conclusions and Lessons Learned In this paper, we have described TinderBook, a book recommender system that addresses the "new user" problem using knowledge graph embeddings. The knowledge graph is built using data from LibraryThing, containing book ratings from users, and DBpedia. We have explained the methodological underpinnings of the system, reporting an offline experiment showing that the entity2rec item relatedness based on knowledge graph embeddings is outperforming a purely collaborative filtering algorithm (ItemKNN) as well as a purely content-based system based on RDF2Vec. This is in line with the claim that hybrid recommender systems typically outperform purely collaborative and content-based systems. Then, we have provided a high-level description of the application, showing the typical usage session, the architecture and how user interactions are mapped to server API calls.
g. abstract), without the cost of creating, curating and maintaining a book database. The multilinguality of DBpedia will also be a great advantage when, in the future, we will extend TinderBook to multiple languages. On the other hand, using DBpedia data has some pitfalls. The first one is the data loss during the mapping, as only 11,694 out of a total of 37,231 books (31. 409%) in the LibraryThing dataset are mapped to DBpedia entities 8. The second one is that, in some cases, the information in DBpedia resulted to be inaccurate. For example, during some preliminary tests, we have noticed that in many cases the thumbnail reported in the 'dbo:thumbnail' property is far from ideal to represent accurately the book (see Jurassic Park novel  9  ), and we had to rely on Google to find better book covers.
The loss of coverage and the data quality issues are relevant ones, and they open the question of whether the use of other knowledge graphs might give better results. Finally, it has to be noted that in our specific case, the cost of building the knowledge graph has been strongly mitigated by the re-use of existing DBpedia mappings. The generalization of this approach to a new dataset would require also this effort. In spite of these challenges, users generally give positive feedback about the application, saying that it is fun to use and that recommendations are accurate. So far, it has been used by passionate readers, librarians, students and researchers, and has been promoted through the personal network of its creators, word-of-mouth, as well as popular social media. Although we do not have a precise number, we estimate that, only during the online evaluation of two weeks, more than 100 users have used the application.
Although OpenCL ensures portability, unfortunately, it does not come with performance portability. We have already tuned our code on higher level for various GPU architectures. However, it is clear that, for some kernels as extrsv, additional architecture specific implementations should to be provided. Creffig:extrsv:acc:old provides a comparison for the relative forward error versus the condition number of the double precision, trsv, with the proposed reproducible, extrsv, substitution algorithms. The relative forward error corresponds to x * -x / x * , where x and x * are respectively to the computed and exact solutions. We use the following Skeel formula  [34, 16]  to calculate the condition number of a linear system Ax = b with real coefficients: cond(A, x) = |A -1 ||A||x| x. To perform those these tests, we wrote a random generator of ill-conditioned triangular systems using the algorithm proposed in  [27] ; the random generator ensures that both the matrix L and the right-hand side vector b are composed of double precision floating-point numbers.