dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectra given by our radiative transfer code MontAGN, which includes dust emission, also provide insightful predictions and comparisons. Results. We detect with a fair degree of confidence 54 SSCs of m Ks between 16 mag and 21 mag with a median instrumental uncertainty of 0. 05 mag. When plotted on a color-color diagram and a color-magnitude diagram, it appears that most of the sources are very much extinct with respect to an intrinsic theoretical evolutionary track. Once de-reddened, the colors point unambiguously to two distinct and very recent starburst episodes at 2. 8 and 4. 5 Myr. While the SSCs in the 4. 5 Myr starburst are distributed along the spiral arms, the 2. 8 Myr SSCs are concentrated in the central region. The luminosity and mass functions present a classical power-law behavior, although with shallower slopes than generally observed in LIRGs.
Source extraction We identified cluster candidates using Starfinder  (Diolaiti et al. 2000) , a code developed for the analysis of crowded fields in AO images. An empirical PSF template was first constructed from several bright, non-saturated stars in the subfield, then decontaminated by a preliminary detection at 5σ level. Post-processing including circular masking, principal component extraction, halo smoothing and normalisation were applied. Finally, the sources were iteratively extracted twice at 3σ level. The correlation thresholds were set at 0. 7 for J and K s bands, and 0. 8 for H band. These parameters were tuned to recover the faintest objects while limiting spurious detections. After cross-matching all three bands, we detect 109 objects in total with 15 mag ≤ m K s < 22 mag. Most of them exist in the central region of the galaxy.
Point-spread function photometry For high background variation and crowding, aperture photometry of extragalactic clusters is prone to contamination from neighboring sources and nebular emission  (Bastian et al. 2014) , while PSF photometry performs more robustly, as already demonstrated with GeMS/GSAOI by  Turri et al. (2017). Thus, we used the photometric measurements from Starfinder provided together with the extracted sources. The instrumental photometric errors were calculated from the variance associated with the science data, with median values in J, H, and K s of 0. 05 mag, 0. 03 mag, and 0. 04 mag, respectively. Chavez et al. (2019)  estimate photometric zero points for Gemini's GeMS/GSAOI at various epochs of observation, including the one when the present images were recorded. However, those zero points were derived from standard star observations under natural seeing conditions, and they give colors that are clearly strongly biased when applied to our dataset.
We thus preferred to establish zero points directly from multiple non-saturated 2MASS stars within the original fields instead, using the 2MASS calibration  (Skrutskie et al. 2006). The corresponding errors for J, H, and K s band zero points are 0. 04 mag, 0. 09 mag, and 0. 11 mag. Finally, we corrected the foreground Galactic extinction using the Fitzpatrick (1999) reddening law and  Schlafly & Finkbeiner (2011)  measurements from the NASA/IPAC Extragalactic Database (NED).
Cluster selection To evaluate the potential contamination from foreground stars and background galaxies, we analyzed a nearby control field of the same area in which 42 objects are detected. This is not consistent with the theoretical expectation, since in a field of view of 32 × 32 at the same galactic coordinates of IRAS 17138-1017 and in the apparent K-band magnitude range of 15 mag ≤ m K < 22 mag, the Besançon model  (Robin et al. 2003)  expects negligible Milky Way star count (<1), while the galaxycount program  (Ellis & Bland-Hawthorn 2007)  estimates 12 ± 4 galaxies only, assuming completeness of detection. Now, we cannot exclude a local density that is actually higher than predicted, especially when GalaxyCount is based on galaxy surveys without AO correction, and thus are less sensitive to low surface brightness.
Age distribution and star formation history Figure  5  plots the age distribution of the clusters as an histogram with bins of 0. 1 dex in age. We identify two clear peaks at τ = 2. 8 and 4. 5 Myr. Although rapid changes in the energy distributions and colors of clusters have been known to cause similar statistical artefacts (e. g. ,  Bik et al. 2003; Bastian et al. 2005) , in our case, we believe they are real physical patterns given the very fine age steps of 0. 1 Myr in our models. The ages corresponding to both peaks are consistent with  Randriamanakoto (2015)  who find a broad distribution peaking around 6 Myr and extending to younger ages, but that does not separate into two peaks. Nevertheless, comparisons are limited because they use larger age steps and cannot break the age-extinction degeneracy with BIK sdata only.
In addition, we wondered whether scattered radiation in a highly clumpy medium might lead to a different behavior of the extinction with respect to the wavelength rather than classical reddening laws representing point sources behind a screen. This may be a concern for SSCs, because a significant portion of the light scattered in the cocoon is also collected in the beam 2. To quantify the role of these two effects, we generated a series of simulations using the radiative transfer code MontAGN developed by our team  (Grosset et al. 2018).
Structure-based drug discovery and RNA base pairing networks The central aim of structure-based drug discovery is to identify compounds with high affinity to a given site or set of binding sites. A natural problem to address in this context is the prediction of binding affinity from a binding siteligand pair. Machine learning models which solve this task can be used as alternatives to computationally expensive docking simulations to screen ligand databases for strong binders  (24). And in some cases have shown superior performance to methods built on explicit chemicophysical knowledge  (25). This setting is quite feasible in the protein domain as affinities and drug screens are abundant, hence various methods have been proposed  (26). Recently, some repositories of RNA small molecule data have been made public  (27)  however, only a handful of binding affinities are known.
Contribution RNAmigos brings together domain knowledge of RNA structure, currently available crystal structure data, and graph neural networks, to show that base pairing networks can be used to automatically predict ligands for RNA structures. Importantly, we propose the use of Augmented Base Pairing (ABPNs) networks, an enriched alphabet of base pairing interactions, and demonstrate that they are a necessary component for capturing binding signatures. Molecular fingerprints predicted by RNAmigos serve as ligand search tools across diverse ligand classes and show strong performance in two different ligand screens, as well as compared to a state of the art method, Inforna  (32). Additionally, we explore the use of an unsupervised graph representation learning scheme for boosting model performance in this low-data setting.
The implications of our work are 2fold (i) we show for the first time that we can learn from non-canonical interaction data to make predictions about RNA function and (ii) our ability to enrich for actives in compound libraries shows potential for RNAmigos as an upstream filtering step for more fine-grained drug discovery tools such as docking. The core implementation of RNAmigos is built in Pytorch  (36)  and DGL  (37)  and is available as an open source Python 3. 6 software package.
Dataset preparation We begin by collecting a set of RNA-small molecule complexes from the PDB Data Bank  (38). We download all crystal structures (90% identity threshold) which contain RNA and at least one ligand. This results in 2993 PDB structures. We omit ions such as magnesium (Mg+) from the set of valid ligands as they vastly outnumber organic ligands and likely require customized models. (40)  We choose a maximum allowable distance between any ligand atom and any RNA atom of 10 Angstroms according to David-Eden et al. (16)  which statistically characterized ribosome antibiotic binding sites. The number of valid sites is further reduced when we remove binding sites with fewer than five RNA residues and remove binding sites containing a large proportion of protein residues, (See Supplementary Figure  S1 ).
The final training set consists of 773 binding sites associated to 270 unique ligands. Finally, we build an ABPN from the 3D structure of each binding site identified in the previous step. In the ABPN, each node corresponds to a residue in the binding site, and links/edges are formed between nodes if they form a backbone or base pair interaction. Node and edge annotations are taken from the BGSU RNA 3D Motif Atlas  (14)  database which maintains base pairing annotations of all PDBs with Leontis-Westhof and backbone interac-tion types computed by the software FR3D  (41). In this manner, each ABPNs stores the nucleotide identity (A, U, C, G) of each of its residues as a node attribute, and each base pairing interaction corresponds to an edge with one of 13 different types (backbone + 12 base pairing geometries).
The resulting graphs are on average 15. 76 nodes in size. At this point, the ligand is removed from the structure so that the graph contains only RNA base-pairing information. While atomic coordinates are the current source of data, we highlight that a key feature of taking ABPNs as input is that we can eventually learn from many other sources of ABPN data which are easier to obtain than crystal structures. A promising example comes from recent developments in predicting base pairing networks from RNA sequences in high-throughput  (21, 42). Our model would then be able to directly use such predictions once they are linked to a functional label (such as a ligand in this case). For full details on binding site extraction and graph construction, see Supplementary Material S1. 1.
Fingerprint prediction Given a binding site, our model predicts a set of chemical features which can be used to identify a ligand. This set of features is typically known as a molecular fingerprint  (43). Many approaches to compute fingerprints from chemical structures have been developed; all with the common aim of numerically encoding chemicals  (44) (45) (46). Such encodings greatly facilitate searches for similar compounds in databases and screens. In this work, we use a common fingerprint implementation known as the MDL Molecular Access Keys (MACCS) fingerprint  (47)  which has the advantage of providing compact and interpretable entries. In training mode, it is paired with a native ligand (Target) from which a target fingerprint y is constructed. The embedding network (RGCN) produces a matrix of node embeddings of dimension n × d where n is the number of nodes in the graph, and d is a fixed embedding size.
This is followed by a pooling step which reduces node embeddings to a single graph-level vector. Finally, the graph representation is fed through a multi-layer perceptron (MLP) to produce a predicted fingerprint ŷ that minimizes the distance L c to the native fingerprint y. The fingerprint is then used to search for similar ligands to the prediction in a ligand screen and thus enriches the probability of identifying an active compound. The RGCN network is pre-trained using an unsupervised node embedding framework which allows us to leverage structural patterns from a large dataset of RNA structures. This network is trained to generate embeddings which minimize the distance (L r ) between kernel similarities k(u, v) and embedding similarities z u , zv. ence or absence of a chemical property.
We construct two decoy sets for our experiments. Since there are currently no experimentally validated data sets of active and inactive binders for a given RNA site (such as DUDE for protein (54)), our first set consists of all RNAbinding ligands in the PDB (270 ligands). The second decoy set is constructed using DecoyFinder (55) on default settings, which samples a list of 36 decoys for each compound such that generic chemical properties are preserved while potentially disturbing binding potential. Of course, this test assumes that chemical dissimilarity between an active compound implies inactivity which is not always the case  (56). However, the current aim of our work is simply to determine whether ABPNs retain significant amount of information about its observed ligand preferences, for which this test is sufficient.
RESULTS We report resulting rank over the list of all RNA-small molecule pairs as well as the set of all decoys for each ligand, following the two decoy benchmark process. Due to the limited size of our labeled data set, we performed a 10-fold cross-validation to include all training pairs in the evaluation and provide a more accurate measure of performance. All results are reported from the held-out sets in our validation, hence the model is never trained on the same binding sites that are being predicted on. Node embeddings are computed using a three-layer RGCN, each layer consisting of 16 dimensional inputs and outputs, a graph attention layer computing a 16dimensional graph embedding and a fully-connected layer, which outputs a 166-dimensional vector. See Supplementary Table  S1  for full model architecture and hyperparameters.
Interestingly, the class that is most consistently predicted accurately corresponds to the aminoglycosides (highlighted in the green cluster in the middle). Aminoglycosides are a class of antibiotics binding to bacterial RNA with well-defined binding sites  (57) , and are quite abundant in the dataset. Nucleic acid-like compounds, many of which bind riboswitches, also form a large family of binders (green) however results were less consistent than for aminoglycosides. A possible explanation for strong performance on aminoglycosides, apart for the larger number of examples obtained, is that these are typically large polysaccharide-like structures with a large number of interactions with the RNA. On the other hand, riboswitches bind much smaller molecules with a limited number of interactions.
As a result, binding site requirements are much more complex and specific with aminoglycosides and the large number of interactions can only be fulfilled by a limited number of molecules. We leave this question for future work, as with the current dataset size, we are unable to provide quantitative evidence of such phenomena. Finally, ligands clustered on the left of the dendrogram show the weakest performance. Since these groups show little branching in the dendrogram, we can conclude that they represent sparsely populated ligand classes for which we have few examples and thus, obtaining more data in these regions could improve performance.
Comparison with a secondary structure-based tool Finally, we compare the performance of RNAmigos with the closest related tool we could find, Inforna  (32). Inforna accepts as input a RNA sequence with a secondary structure and returns a list of candidate ligands, based on sequence and structural similarities with motifs stored in a database. Although the input is not strictly identical, it is quite close to the one of RNAmigos. Similarly for the output, Inforna provides direct ligand information. In this benchmark, we provide to Inforna secondary structures computed with Forgi (58) directly from the PDB files, which is the most accurate input available. For each chain in each RNA PDB associated with a ligand in the PDB databank set, we query the Inforna web works contain useful ligand binding information.
The significance of our results is 2-fold. We show for the first time that ABPNs encode sufficient information for a classification task, and establish an initial methodological primitive for such a task. To date, the majority of computational methods which leverage ABPNs have focused on sequence to structure  (21, 59 ) prediction and motif identification  (14, 20). While these tasks involve some degree of learning, the relevance of higher-order interactions lies ultimately in their potential to specify function, which until now has been left unexplored. Interestingly, these findings come at a time when information of the type our model uses is becoming more widely available. Computational prediction tools such as  (21, 59)  promise to yield large amounts of higher-order RNA pairwise interaction data without need for costly crystallography experiments.
This opens the door to applying such data in other important biological problems such as RNA binding protein prediction  (60)  and ion binding  (40). Furthermore, the promising results obtained from the unsupervised pre-training provide a methodological building block for assisting in supervised learning on complex RNA structures. Second, our findings take a first step towards learningbased methods for systematically identifying drugs binding to RNA, and pinpoint ABPNs as essential tools for this task. The finding that only ABPN representations of binding sites was able to produce a significant signal in the task indicates that richer representations are necessary for successful classification when complex interactions are at play. Since our prediction is a fingerprint vector (chemical descriptor) and not a simple classification of ligands (i.
e directly selecting a single ligand as output, or predicting an affinity), the fingerprint itself can be used to search large ligand databases, and can eventually be applied to direct molecule generation  (25). While performance was strong across different ligand classes, it is apparent that classes for which data is more abundant received more consistently positive predictions. Therefore, as more examples of RNAligand complexes are characterized by experimental and computational techniques, we believe that the performance of our platform will improve. Additional data will also allow us to account for properties desired in medical applications such as synthesizability, and drug-likeness  (61). Our choice of graphs for binding site representation reflects this consideration, as graphs can natively hold additional information such as evolutionary or chemical properties without requiring changes to the pipeline.
A collection of interesting and practical quantum protocols, ranging from quantum communication and quantum cryptography to quantum error correction, have been specified and verified. In other recent work, a version of the quantum process calculus CQP has been implemented. The implementation, which has the working title qtpi and is available from github. com/mdxtoc/qtpi, uses symbolic rather than numeric probability calculation. Programs are checked statically, before they run, to ensure that they obey real-world restrictions on the use of qbits (e. g. no cloning, no sharing). Qtpi has been used to simulate some simple protocols such as teleportation, and some more involved ones including QKD. It is early days in the development of the tool, but it can already simulate well over 1M qbit transfers per minute.
The implementation exploits algebraic data types to faithfully represent three core ingredients: a process calculus, multiparty session types, and forward and backward reduction semantics. This implementation bears witness to the convenience of pure functional programming for implementing reversible languages. In a series of works  [11, 16]  multiparty session types (aka global types) have been enriched with checkpoint labels on choices that mark points of the protocol where the computations may roll back. In  [16] , a simple model is developed in which rollback could be done any time after a participant has crossed the checkpointed choice. In  [11]  a more refined model is presented, in which the programmer can define points where the computation may revert to a checkpointed label, and rollback has to be triggered by the participant that made the decision.
Regions are sufficient to support the implementation of standard OO programming patterns and a collection hierarchy. The initial presentation of the ROOPL language relied exclusively on stack allocation  [29] , and was subsequently extended with a reversible heap-based memory manager  [13]  based on Knuth's Buddy Memory algorithm  [36]. With this extension, data structures such as min-heaps and circular buffers can be implemented  [13]. The language is reversibly universal (r-Turing complete), which means it has the computational power of reversible Turing machines (cf. [71] ). See Figs. 1, 2, and 3 for example programs in Joule and ROOPL, which will be described in the next section.
The study was further developed in  [42] , where relevant properties of the approach were proved, e. g. , that the rollback operator indeed satisfies the constraints of causal-consistent reversibility. The focus on debugging started in  [41] , where CauDEr  [40] , a Causal-consistent Debugger for (core) Erlang, was described. CauDEr provided the primitives above for causalconsistent reversible debugging, paired with primitives for forward execution and with a graphical interface to show the runtime structure of the program under analysis and the relevant concurrent events in the computation. A main limitation of CauDEr was that if the user went too far back, there was no automatic way to go forward again with the guarantee to replay the misbehaviour under analysis. This is a relevant problem, since in concurrent systems misbehaviours depend on the scheduling, and of course it is not possible to debug a misbehaviour that does not appear when executing the wrong application inside the debugger.
To solve this problem, the research studied techniques for tracing a computation and replay it inside the debugger. This lead to the definition of a new form of replay, called causal-consistent replay  [43] , which allows one to redo a future event of a traced computation, including all and only its causes. One can notice that causal-consistent reversibility and causalconsistent replay are dual, and together they allow one to explore a wrong computation back and forward, always concentrating on events of interest. Also, this approach ensures that if a misbehaviour occurred in the traced computation then the same misbehaviour occurs also in each possible replay (provided that execution goes forward enough). A tracer for Erlang compatible with CauDEr was produced and is available at  [39]. An example of application of this framework to a simple Erlang program can be found in  [21].
All the endeavours listed above have been implemented in C/C++ and made publicly available at http://iic. jku. at/eda/research/quantum simulation. Besides that, a stand-alone version of the developed DD-package is available at http://iic. jku. at/eda/research/quantum dd. Together with the significant improvements gained compared to the state of the art, this did not only result in acknowledgement inside the academic community, but also received interest from big players in the field. More precisely, the developed simulation approach has been acknowledged with a Google Research Faculty Award and has recently been officially integrated into IBM's SDK Qiskit. This further emphasises the potential of DD-based design methods in the quantum domain-hopefully leading to as powerful DD-based methods as taken for granted in the conventional domain today.
For the "quantum part" of an algorithm, a decomposition into multiplecontrolled qubit gates is usually inherently given by the algorithm, by using common building blocks like a Quantum Fourier Transform (QFT  [31] ), or determined by hand. However, this is different for large Boolean components that are contained in many quantum algorithms, e. g. , the modular exponentiation in Shor's algorithm for integer factorisation  [3]  or a Boolean description of the database that is queried in Grover's algorithm  [2]. Even though the functionality of the Boolean components can be described in the conventional domain, corresponding design methods cannot be utilised since the inherent reversibility of quantum computations has to be considered. In fact, determining circuits composed of reversible gates only, requires dedicated reversible-circuit synthesis approaches.