dataesr/scientific-paragraphs-categorization huggingface dataset:
IV. AUTOMATED RESOURCE ANALYSIS OF ORACLES VIA THE PROGRAMMING LANGUAGE QUIPPER The logical circuits required to implement the Oracles A, b, and R were generated using the quantum programming language Quipper and its compiler. Quipper is also equipped with a gate-count operation, which enables performing automated LRE of the oracle implementations. Our approach is briefly outlined as follows. Oracles A, b and R were provided to us in the IARPA QCS program GFI in terms of Matlab functions, which return matrix and vector elements defining the original linear-system problem. The task was to implement them as unitary quantum circuits. We used an approach that combines 'Template Haskell' and the 'classical-to-reversible' functionality of Quipper, which are explained below. This approach offers a general and automated mechanism for converting classical Haskell functions into their corresponding reversible unitary quantum gates by automatically generating their inverse functions and using them to uncompute ancilla qubits.
A. Quipper and the Circuit Model The programming language Quipper  [14, 15]  is a domain specific, higher-order, functional language for quantum computation. A snippet of Quipper code is essentially the formal description of a circuit construction. Being higher-order, it permits the manipulation of circuits as first-class citizens. Quipper is embedded in the host-language Haskell and builds upon the work of  [31, 32, 33, 34, 35]. In Quipper, a circuit is given as a typed procedure with an input type and an output type. For example, the Hadamard and the not-gates are typed with hadamard :: Qubit -> Circ Qubit qnot :: Qubit -> Circ Qubit They input a qubit and output a qubit. The keyword Circ is of importance: it says that when executed, the function will construct a circuit (in this case, a trivial circuit with only one gate).
Quantum data-types in Quipper are recursively generated: Qubit is the type of quantum bits; (A,B) is a pair of an element of type A and an element of type B; (A,B,C) is a 3-tuple; () is the unit-type: the type of the empty tuple; [A] is a list of elements of type A. If a program has multiple inputs, we can either place them in a tuple or use the curry notation (→). For instance, the program prog :: (A,B,C) -> Circ D takes three inputs of type A, B and C and outputs a result of type D, while at the same time producing a circuit. Using the curry notation, the same program can also be written as prog :: A -> B -> C -> Circ D where D is the type of the output. We use the program by placing the inputs on the right, in order: prog a b c The meaning is the following: prog a is a function of type B -> C -> Circ D, waiting for the rest of the arguments; prog a b is a function of type C -> Circ D, FIG.
B. Quipper-Generated Resource Estimation The previous section showed how a program in Quipper is essentially a description of a circuit. The execution of a given program will generate a circuit, and performing logical resource estimation is simply achieved by completing the program with a gate-count operation at the end of the circuit-generation process. Instead of, say, sending the gates to a quantum co-processor, the program merely counts them out. Quipper comes equipped with this functionality.
D. Quipper and Template Haskell As the transformation sending a procedure f to a circuit T f is compositional, it can be automated. We are using a feature of the host language Haskell to perform this transformation automatically: Template-Haskell. In a nutshell, it allows one to manipulate a piece of code within the language, produce a new piece of code and inject it in the program code. Another (slightly misleading) way of saying it is that it is a type-safe method for macros. Regardless, it allows one to do exactly what we showed in the previous section: function composition is transformed into circuit composition, and every subfunction f : A → B is replaced with its corresponding circuit, whose type 14 is A → Circ B: a function that inputs an object of type A, builds a (piece of) circuit, and outputs B.
|0 x g(x) x As Quipper is a high-level language, it flawlessly allows circuit manipulation. In particular, one can perform the meta-operation classical to reversible sending the circuit T f to U f , of type |0 • • • 0 T h |0 h(g(x)) garbage |0 • • • 0              FIG. 23. Composing two oracles. T f |0 x • x |0 • • • 0 T -1 f z ⊕ z + f (x) (A → Circ B) → (A, B) → Circ (A, B), in[0] in  [1]  in  [2]  out 0 0 0 0 in[0] in  [1]  in  [2]  out FIG. 26. Circuit made reversible. provided that A and B are essentially lists of qubits, and that T f only consists of classical reversible gates: nots, c-nots, cc-nots, etc. In the case of our my and function, it produces the circuit in Fig. 26  of the correct shape. One can easily check that the wire out is correctly set.
E. Encoding Oracles The oracles of QLSA were given to us as a set of Matlab functions as part of the IARPA QCS program GFI. These functions computed the matrix A and the vectors b and R of  [5]. They were not using any particular library: directly translating them into Haskell was a straightforward operation. As the Matlab code came with a few tests to validate the implementation, by running them in Haskell we were able to validate our translation. The main difficulty was not to translate the Matlab code into Quipper, but rather to encode by hand the real arithmetic and analytic functions that were used. Fig. 27  shows a snippet of translated Haskell code: it is a nontrivial operation using trigonometric functions. Another part of the oracle is also using arctan. To be able to be processed through Template Haskell, all the arithmetic and analytic operations had to be written from scratch on integers encoded as lists of Bool.
These recursive gate-decomposition rules are coded in the symbolic programming software Mathematica for computing the final estimates. Oracle A returns either the magnitude (argflag = 20 As discussed previously, our circuit-implementations of oracles are essentially the trace of execution of a classical program of an algorithm. Because the algorithms we used are purely sequential, the corresponding quantum circuits are not easily parallelizable on a global scale. The only possible optimizations are purely local. We therefore conclude that our computed circuit-and Tdepth values are over-estimates by some unknown small factor wrt. optimal depth values. False) or the phase (argflag = True) of the coupling weight and the connected node index at the chosen matrix-decomposition-band index (from 1 to N b = 9).
This is indeed true in all cases for which we have performed resource counting. The two representative tables for argflag = False and argflag = True are presented in Table  IX. Finally, the resource counts for Oracle r and for Oracle b are done similarly: Quipper gives logical resource estimates, then recursive gate-decomposition rules are coded in the symbolic computing software Mathematica for computing the final estimates presented in Table  X. One may wonder why our oracle implementations require such a huge number of auxiliary qubits and measurements -namely, up to ∼ 10 8 ancilla qubits and measurements for a problem size N ≈ 3 × 10 8. This indeed is a feature of our low-level implementation of the irreversible-to-reversible transformations that is similar to the way "logical reversibility of computation" was proposed by Bennett in  [53].
Acknowledgements This research have been supported by the  Warsaw Centre of Mathematics and Computer Science (Jean-Florent Raymond) , by the  (Polish) National Science Centre  Grant  PRELUDIUM 2013/11/N/ST6/02706  ( Jean-Florent Raymond ), and by the  E. U. (European Social Fund-ESF ) and Greek national funds through the  Operational Program "Education and Lifelong Learning " of the National Strategic Reference Framework (NSRF)- Research Funding Program : "Thales. Investing in knowledge society through the  European Social Fund " ( Dimitris Chatzidimitriou  and  Dimitrios M. Thilikos ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Next generation 5G networks will rely on virtualized Data Centers (vDC) to host virtualized network functions on commodity servers. Such Network Function Virtualization (NFV) will lead to significant savings in terms of infrastructure cost and reduced management complexity. However, green strategies for networking and computing inside data centers, such as server consolidation or energy aware routing, should not negatively impact the quality and service level agreements expected from network operators. In this paper, we study how robust strategies that place virtual network functions (VNF) inside vDC impact the energy savings and the protection level against resource demand uncertainty. We propose novel optimization models that allow the minimization of the energy of the computing and network infrastructure which is hosting a set of service chains that implement the VNFs.
Acknowledgements This research has been partially supported by  MATH-AmSud  grant  16-MATH-05 SM-HCD-HDD  ( C. Aaron  and  A. Cholaquidis ) and  Spanish  grant  MTM2016-78751-P  ( A. Cuevas ). We are grateful to  Luis Guijarro  and  Jesús Gonzalo  ( Dept. Mathematics ,  UAM, Madrid ) for useful conversations and advice.
Furthermore, we have shown in the previous section that the solutions found by PDA, and thus the clusters found, are of better quality than its competitors for all instances. This shows the usefulness of data perturbation: by modifying in a non linear way the search directions, PDA has access to clusters hardly attainable with the other methods. We can notice that the performances of PDA shown in the previous section are strongly correlated with the additional number of clusters found by our method than its competitors. For example, given the ClusterAB500 instance,.
Corpus annotation for English TQs The first step is to produce annotated data. The corpora used cover three language pairs: CS-EN, DE-EN and FR-EN, and are large subsets of the most recent films of the OpenSubtitles 2  parallel corpus  (Lison and Tiedemann, 2016). The subtitles were automatically cleaned using heuristics and processed with the MElt tokeniser  (Denis and Sagot, 2012)  and the Moses truecaser  (Koehn et al. , 2007). We then developed robust, manually defined lexical rules to identify English TQs 3. We identify both the presence of TQs and the question tags themselves (e. g. is it?, right?). A manual evaluation on a random subset of 500 grammatical TQs and 500 lexical TQs shows that annotations are near perfect (accuracy of ≈98% and recall of 100% on sentence-final grammatical TQs whose host clause is in the same subtitle).
Acknowledgments We thank the three anonymous reviewers of the  EACL Student Research Workshop , as well as or colleagues  Tal Linzen ,  Maximin Coavoux  and  Sacha Beniamine  for their comments, questions and suggestions on the paper. We also thank  Adeline Nazarenko , our thesis co-director, her  labthe Laboratoire d'Informatique de Paris  Nordand the members of our thesis advisory committee -Saveria Colonna and Isabelle Tellier -for their support on this work. Finally, we thank our lab engineer  Doriane Gras , for her help with the statistics. This work was supported by the  Labex Emperical Foundations of Linguistics  ( ANR-10-LABX-0083 ) and the doctoral school Frontières du Vivant.
SuB  20 , and the 2016 Workshop on Co-Distributivity. The research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement No. 324115-FRONTSEM (PI: Schlenker). Research was conducted at  Institut d'Etudes Cognitives (ENS) , which is supported by grants  ANR-10-IDEX-0001-02 PSL*  and  ANR-10-LABX-0087 IEC. ©2017   Jeremy Kuhn  and  Valentina Aristodemo This  is an open-access article distributed under the terms of a Creative Commons.
Constraining each component to the interval (-1, 1) ensures the stability of the model and there is a one-to-one mapping between a t and ρ t. The signal Z t is observed in noise, Y t = Z t + V t , with V t ∼ N (0, σ 2 v ). The static parameters of the model are assumed to be known, with values given in the Supplementary Material. The latent process X t = Z t , ρ t , ξ t T ∈ R P+2 is Pth-order Markov. The Supplementary Material details a straightforward generalization of the blocked particle Gibbs samplers to take into account this lag-P dependence at the block boundaries. We use a simulated dataset with n = 2000 and P = 4. The methods considered are: (i) standard particle Gibbs by  Andrieu et al. (2010) ; (ii) particle Gibbs with ancestor sampling by  Lindsten et al. (2014) ; and (iii) the proposed parallel block sampler with different block sizes L and overlaps p, where (L, p) ∈ {(10, 0), (50, 0), (50, 10)}.
1e-2), and TRAM/LAG1/CLN8 homology domain (1. 0% of preys, P = 1. 2e-2). We also investigated whether pairs of protein domains or conserved sites (one on a bait and the other on a prey) were enriched among interacting protein pairs. Top enriched pairs (adjusted P < 2. 7e-12) included bait domain GPCR, rhodopsin-like, 7TM (IPR017452) paired with prey domains/sites Tetraspanin, conserved site (IPR018503), Tetraspanin, EC2 domain (IPR008952), and Marvel domain (IPR008253). A significant number of bait GPCRs are already targeted by drugs (28 of 48 proteins, P = 3. 1e-8 relative to all proteins; Fig  3 ). These drugs comprise a variety of categories, such as histamine antagonists, antiparkinson agents, and antipruritics agents, and affect diverse organ systems, including nervous, cardiovascular, and respiratory (Fig  EV3 ).
Orthogonal validation of MYTH-identified PPIs in mammalian cells As a secondary validation of our GPCR interactome, a subset of PPIs selected from our interaction data was tested in mammalian cells using two distinct co-immunoprecipitation (co-IP) approaches. In the first approach, FLAG-tagged GPCR interactors were overexpressed in mammalian cells, pulled-down using anti-FLAG antibody, subjected to SDS-PAGE, transferred to membranes, and probed with commercial antibody raised against their identified endogenous GPCR-interaction partner. We tested a subset of interactions corresponding to 11 different GPCR proteins, using four MYTH-identified interacting preys and two non-interacting negative control preys for each. Of the 11 GPCR baits, five performed well in our analysis, producing no more than background signal in at least one of two negative control samples, from which we were able to confirm a total of 13 (65%) of tested interactions (Figs 4A and EV4, Table  EV6 ).
Overall, however, the strong confirmation rate obtained using our orthogonal test approaches extensively supports the quality of our MYTH GPCR interactome dataset. ◀ Figure  5. Functional interactions of GPR37 and GPRIN2 with 5-HT4d in transfected cells. A Co-immunoprecipitation in the presence and absence of 1 lM 5-HT agonist for 15 min. HEK-293 cells were transiently transfected with 5-HT4d-YFP (lanes 2, 3, 5, 6) and myc-GPRIN2 (lanes 1-3) or GPR37 (lanes 4-6) and processed for immunoprecipitation using an anti-GFP antibody. The crude extracts (lysate) and immunoprecipitates (IP) were analyzed by SDS-PAGE and immunoblotted using a rabbit anti-GFP or anti-Myc antibody. Data are representative of at least two independent experiments. B BRET donor saturation curves were performed by co-transfecting a fixed amount of 5-HT4d-Rluc and increasing amounts of 5-HT4d-YFP, GPR37-YFP, and GPRIN2 We also carried out additional, in-depth functional validation on selected GPCR PPIs identified in our interactome using biochemical and cell-based assays as well as knockout and knock-in animals.
These focused analyses of novel GPCR interactions further demonstrate the utility of our MYTH-based GPCR interactome as a powerful resource for biological research in this area. In summary, we report here the largest, most comprehensive interactome study of full-length, human GPCRs carried out directly in the context of living cells. All of the data generated in this work is freely available for use by the scientific community [see the Expanded View and online in the IID database  (Kotlyar et al, 2016) ]. Additionally, we have performed preliminary functional validation of a selection of PPIs, which should serve as a starting point for further work. Our GPCR-interactome data, particularly when combined with other collaborative projects, such as the GPCR Network  (Stevens et al, 2012)  and the mapping of GPCR interaction networks performed using other recently developed technologies, such as CHIP-MYTH  (Kittanakom et al, 2014)  and the mammalian membrane two-hybrid (MaMTH;  Petschnigg et al, 2014; Yao et al, 2017) , will contribute significantly to our understanding of the chemistry and biology of these clinically relevant proteins, serving as an important tool to further our knowledge of cell signaling processes and helping identify novel biologically important interactions for use in the development and improvement of therapeutic strategies.
mBU, mBRET units. Data shown are from three independent experiments. E Cell surface expression of HEK-293 cells transiently transfected with cDNA encoding ADORA2A (lane 1), GPR37-YFP (lane 2) or ADORA2A plus GPR37-YFP (lane 3). Cell surface proteins were biotinylated and crude extracts (whole cell) and biotinylated proteins were subsequently analyzed by SDS-PAGE and immunoblotted (IB) using a rabbit anti-GPR37 antibody (1/2,000) or a rabbit anti-A 2A R antibody (1/2,000). F Cell surface expression of HEK-293 cells transiently transfected with cDNA encoding ADORA1 (lane 1), GPR37-YFP (lane 2), or ADORA1 plus GPR37-YFP (lane 3). Cell surface proteins were biotinylated and crude extracts (whole cell) and biotinylated proteins were subsequently analyzed by SDS-PAGE and immunoblotted (IB) using a rabbit anti-GPR37 antibody (1/2,000) or a rabbit anti-A 1 R antibody (1/2,000).
Confirmation of known GPCR interactions by MYTH Known GPCR-interacting partners were identified from the Integrated Interactions Database (IID)  (Kotlyar et al, 2016). Gateway compatible ORFs were obtained from the Human ORFeome Collection version 8. 1  (Yang et al, 2011)  and used, via the Gateway system (Life Technologies), to generate either N-terminally tagged preys in pGPR3N (Dualsystems Biotech) or C-terminally tagged preys in pGLigand (created in-house, Stagljar lab) depending on which end is available for tagging. All bait prey interaction tests were carried out using MYTH as previously described  (Snider et al, 2010)  in the NMY51 yeast reporter strain. Note that prior to use in interaction tests with GPCR baits all preys were tested for promiscuity by use of an artificial bait construct that consists of the singlepass transmembrane domain of human T-cell surface glycoprotein CD4 and the Cub-TF tag  (Snider et al, 2010)  and by use of the yeast protein RGT2.
Filtering interactions To reduce the number of false positives, we eliminated detected interactions involving preys that carry out signal peptide processing (GO:0006465) and ribosomal contaminants  (Glatter et al, 2009). We identified these preys using Gene Ontology (GO;  Ashburner et al, 2000)  annotations from the UniProt-GO Annotation database  (Matthews et al, 2009; Dimmer et al, 2012) , downloaded through the EMBL-EBI QuickGO browser  (Binns et al, 2009 ; http://www. ebi. ac. uk/QuickGO/GTerm?id=GO:0006465#term=annotation), on September 10, 2016.
Annotating interacting proteins: membrane localization Baits and preys localized to the plasma membrane were identified using GO annotations from the UniProt-GO Annotation database  (Dimmer et al, 2012) , obtained through the EMBL-EBI QuickGO browser  (Binns et al, 2009 ; http://www. ebi. ac. uk/QuickGO/GTe rm?id=GO:0006465#term=annotation) on August 31, 2016.
Pathway annotations Pathway annotations for baits and preys, as well as pathway enrichment analysis, were performed using the pathDIP database Molecular Systems Biology 13: 918 | 2017 ª 2017 The Authors Molecular Systems Biology GPCR-interactome mapping Kate Sokolina et al  (Rahmati et al, 2017)  ver. 2. 5 (http://ophid. utoronto. ca/pathDIP), using the setting "Extended pathway associations" with default parameters. P-values were FDR-corrected using the Benjamini-Hochberg method.
Molecular function and biological process annotations and enrichment analysis Molecular function and biological process Gene Ontology annotations were downloaded from Gene Ontology Consortium (Gene Ontology Consortium, 2015) on November 30, 2016. Enrichment of preys for molecular functions was calculated using the topGO library version 2. 24. 0 in R version 3. 3. 1  (Alexa & Rahnenfuhrer, 2016). A topGOdata object was created with nodeSize = 10, and the runTest function was used with the default algorithm (weight01) and statistic = fisher. P-values were adjusted for multiple testing using the Benjamini-Hochberg method. Enrichment of preys for biological processes was calculated the same way.
Drug target enrichment and drug category enrichment Drug targets and drug therapeutic categories were downloaded from DrugBank version 5  (Wishart et al, 2006). Enrichment of drug targets among GPCR baits was calculated as a hypergeometric P-value, using the following parameters: the number of human protein-coding genes in the HGNC database  (Gray et al, 2015) (19,008) , the number drug targets in DrugBank (4,333), the number of baits (48), and the number of baits that are drug targets (28). Enrichment of therapeutic categories among baits and preys was calculated as hypergeometric P-values using the following parameters: the number of human protein-coding genes in the HGNC database  (Gray et al, 2015) (19,008) , the number of targets in a therapeutic category, the number of baits and preys (686), and the number of baits and preys that are targets in the category.
Acknowledgements We thank  K. Seuwen  ( Novartis ) for discussions during initiation phase of the project and for providing bait cDNA. The work in the Stagljar laboratory was supported by grants from the  Canadian Institutes of Health Research (CIHR , # MOP-106527 ),  Canadian Foundation for Innovation, Natural Sciences and Engineering Research Council of Canada ,  Ontario Genomics Institute ,  Canadian Cystic Fibrosis Foundation ,  Canadian Cancer Society and Ontario Research Fund (University Health Network ). The work in the Jockers laboratory was performed within the  Département Hospitalo-Universitaire (DHU) AUToimmune and HORmonal diseaseS  and supported by grants from the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Fondation Recherche Médicale (Equipe FRM   DEQ20130326503  to R.
This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category. Previous approaches focus on either combining a spatial regularizer with hand-crafted features, or learning a correspondence model for appearance only. We propose instead a convolutional neural network architecture, called SCNet, for learning a geometrically plausible model for semantic correspondence. SCNet uses region proposals as matching primitives, and explicitly incorporates geometric consistency in its loss function. It is trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and a comparative evaluation on several standard benchmarks demonstrates that the proposed approach substantially outperforms both recent deep learning architectures and previous methods based on hand-crafted features.