dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, the exponent β remains practically the same for the WWW  (Donato et al. , 2004)  and the PCN of the Linux Kernel  (Chepelianskii, 2010) , while the values of fractal dimension are different with d ≈ 4 for the WWW and d ≈ 1. 3 for the PCN  (Ermann, Chepelianskii, and Shepelyansky, 2011). The analysis of the eigenstates of G and G Ã shows that their IPR values remain small (ξ < 70) compared to the matrix size N ≈ 2. 8 × 10 5 showing that they are well localized on certain selected nodes.
VIII. WWW NETWORKS OF UK UNIVERSITIES The WWW networks of certain UK universities for the years between 2002 and 2006 are publicly available at http:// cybermetrics. wlv. ac. uk/database/. Because of their modest size, these networks are well suitable for a detailed study of PageRank, CheiRank, complex eigenvalue spectra, and eigenvectors  (Frahm, Georgeot, and Shepelyansky, 2011).
From Frahm, Georgeot, and  Shepelyansky, 2011. ln K þ ln K Ã ¼ const. This is similar to the properties of the density distribution for the Wikipedia network shown in Fig. 7(a). The 2DRanking might give new possibilities for information retrieval from large databases which are rapidly growing with time. For example, the size of the Cambridge network increased by a factor of 4 from 2006 to 2011. At present, Web robots start automatically to generate new Web pages. These features can be responsible for the appearance of gaps in the density distribution in the ðK; K Ã Þ plane at large K; K Ã ∼ N values visible for large-scale university networks such as ENS Paris in 2011 (see Fig. 21 ). Such an automatic generation of links can change the scale-free properties of networks. Indeed, for ENS Paris a large step in the PageRank distribution appears  (Ermann, Chepelianskii, and Shepelyansky, 2012)  possibly indicating a delocalization transition tendency of the PageRank that can destroy the efficiency of information retrieval from the WWW.
7. This result is in good agreement with the fact that the correlator κ between PageRank and CheiRank vectors is rather large for Wikipedia κ ¼ 4. 08 while it is close to zero for the Linux network κ ≈ -0. 05. The difference between PageRank and CheiRank is clearly seen from the names of the articles with the highest ranks [ranks of all articles are given in  Zhirov, Zhirov, and Shepelyansky (2010) ]. At the top of PageRank we have (1) the United States, (2) the United Kingdom, and (3) France, while for CheiRank we find (1) Portal: Contents/Outline of Knowledge/Geography and Places, (2) a list of state leaders by year, and (3) Portal: Contents/ Index/Geography and Places. Clearly PageRank selects first articles on a broadly known subject with a large number of ingoing links while CheiRank selects first highly communicative articles with many outgoing links.
The 2DRank combines these two characteristics of information flow on a directed network. At the top of 2DRank K 2 we find (1) India, (2) Singapore, and (3) Pakistan. Thus, these articles are most known and popular and most communicative at the same time. The top 100 articles in K; K 2 ; K Ã are determined for several categories including countries, universities, people, and physicists. It is shown in  Zhirov, Zhirov, and Shepelyansky (2010)  that PageRank recovers about 80% of the top 100 countries from the SJR database  (SJR, 2007) , about 75% of the top 100 universities of Shanghai University ranking, 1  and, among physicists, about 50% of the top 100 Nobel winners in physics. This overlap is lower for 2DRank and even lower for CheiRank. However, as we see in more detail, 2DRank and CheiRank highlight other properties being complementary to PageRank.
If a person does not belong to any of the studied editions then he or she is attributed to an additional cultural group world WR. After such an attribution of all persons the two networks of cultures are constructed based on the top PageRank historical figures and the top 2DRank historical figures, respectively. Each culture (i. e. , language) is represented as a node of the network, and the weight of a directed link from culture A to culture B is given by the number of historical figures belonging to culture B (e. g. , French) appearing in the list of top 100 historical figures for a given culture A (e. g. , English). For example, according to  Eom et al. (2015) , there are five French historical figures among the top 100 PageRank historical figures of the English Wikipedia, so we can assign weight 5 to the link from English to French.
Thus, Figs. 28(a  In total we have two networks with 25 nodes which include our 24 editions and an additional node WR for all other world cultures. Persons of a given culture are not taken into account in the rank list of the language edition of this culture. Then following the standard rules (1) the Google matrix of the network of cultures is constructed by the normalization of the sum of all elements in each column to unity. The matrix G KK 0 , written in the PageRank indices K; K 0 , is shown in Fig. 29  for persons from (a) PageRank and (b) 2DRank lists. The matrix G Ã is constructed in the same way as G for the network with inverted directions of links. From the obtained matrices G and G Ã we determine PageRank and CheiRank vectors and then the PageRank-CheiRank plane ðK; K Ã Þ, shown in Fig.
However, we argue that for cultures it is also important to have strong communicative properties and hence it is important to have a 2DRank of cultures at top positions. On the top 2DRank position we have Greek, Turkish, and Arabic (for PageRank persons) in Fig. 30 (a) and French, Russian, and Arabic (for 2DRank persons) in Fig. 30(b). This demonstrates the important historical influence of these cultures via both importance (incoming links) and communicative (outgoing links) properties present in a balanced manner. Thus the described research across Wikipedia language editions suggests a rigorous mathematical way, based on Markov chains and Google matrix, for the recognition of important historical figures and the analysis of interactions of cultures at different historical periods and in different world regions.
, 2007 (Burgos et al. , , 2008;; Bastolla et al. , 2009; Saverda et al. , 2011). Using the UN COMTRADE database we show that a similar ecological analysis gives a valuable description of the world trade: countries and trade products are analogous to plants and pollinators, and the whole trade network is characterized by a high nestedness typical for ecological networks. An important feature of ecological networks is that they are highly structured, being very different from randomly interacting species  (Bascompte et al. , 2003). Recently it was shown that the mutualistic networks between plants and their pollinators  (Bascompte et al. , 2003; Memmott, Waser, and Price, 2004; Vázquez and Aizen, 2004; Olesen et al. , 2007; Rezende et al. , 2007)  are characterized by high nestedness which minimizes competition and increases biodiversity  (Burgos et al.
, 2007 (Burgos et al. , , 2008;; Bastolla et al. , 2009; Saverda et al. , 2011). The mutualistic WTN is constructed on the basis of the UN COMTRADE database from the matrix of trade transactions M p c 0 ;c expressed in USD for a given product (commodity) p from country c to country c 0 in a given year  (from 1962 to 2009). For product classification we use 3-digits SITC Rev1 discussed earlier with the number of products N p ¼ 182. All these products are described in UN COMTRADE (  2011  matrix elements m ðiÞ and m ðeÞ in the plane of indices p and c, ordered by the total amount of import and export in a decreasing order, are shown and discussed by  Ermann and Shepelyansky (2013). In global, the distributions of m ðiÞ and m ðeÞ remain stable in time especially in view of 100 times growth of the total trade volume during the period 1962-2009.
Even if the global structure of product ranking by trade volume has certain similarities with import EcoloRanking there are also important new elements. Indeed, in 2008 the mutualistic significance of petroleum products (code 332), machindus (machines for special industries code 718), and medpharm (medical-pharmaceutic products code 541) is much higher compared to their volume ranking, while petroleum crude (code 331) and office machines (code 714) have smaller mutualistic significance compared to their volume ranking. The new element of EcoloRanking is that it differentiates between import and export products while for trade volume they are ranked in the same way. Indeed, the dominant colors for export (Fig. 41 , bottom panel) correspond to food (SITC Rev1 code 0, red) with the contribution of black import and crude materials (code 2, violet); followed by cyan import and a more pronounced presence of finnotclass (commodities and transactions not classified in code 9, brown).
EcoloRanking of export shows a clear decrease tendency of dominance of SITC codes 0 and 2 with time and increase of importance of codes 3 and 7. It is interesting to note that the code 332 of petroleum products is very vulnerable in volume ranking due to significant variations of petroleum prices but in EcoloRanking this product keeps the stable top positions in all years showing its mutualistic structural importance for the world trade. EcoloRanking of export shows also the importance of fish (code 031), clothing (code 841), and fruits (code 051) which are placed on higher positions compared to their volume ranking. At the same time roadvehic (code 732), which is at the top volume ranking, has relatively low ranking in export since only a few countries dominate the production of road vehicles.
It is established there that such multifunctional networks can be analyzed by the Google matrix approach, using a certain personalized vector, so that the world countries are treated on democratic equal grounds while the contribution of products remains proportional to their trade volume. Such a multiproduct world trade network allows one to investigate the sensitivity of trade to price variation of various products. This approach can also be applied to the world network of economic activities obtained from the Organization for Economic Co-operation and Development and the World Trade Organization (OECD-WTO) database  (Kandiah, Escaith, and Shepelyansky, 2015). It allows one to determine the sensitivities of the economic balance of world countries with respect to labor cost variations in certain selected countries.
In difference from the multiproduct WTN of UN COMTRADE, where there are no direct transitions between products, the OECD-WTO database contains interactions between various activity sectors of various countries that opens new possibilities for a more advanced analysis. It is also important to note that usually in economy researchers analyze time evolution of various indexes studying their correlations. The results presented previously for the WTN show that in addition to time evolution there is also evolution in space of the network. As for waves in an ocean, time and space are both important, and we think that time and space study of trade captures important geographical factors which will play a dominant role for the analysis of contamination propagation over the WTN in case of crisis. We think that the WTN data capture many essential elements which will play a rather similar role for financial flows in the interbank payment networks.
At the same time the PageRank-CheiRank correlator is rather small κ ¼ -0. 065. Thus this network is structured in such a way that functions related to order signals (outgoing links of CheiRank) and signals bringing orders (ingoing links of PageRank) are well separated and independent of each other as is the case for the Linux Kernel software architecture. The spectrum of G has a gapless structure showing that long-living excitations can exist in this neuronal network. Of course, model systems of neural networks can provide a number of interesting insights but it is much more important to study examples of real neural networks. Kandiah and Shepelyansky (2014)  performed such an analysis for the neural network of C. elegans (a worm). The full connectivity of this directed network is known and well documented at WormAtlas  (Altun et al.
It is possible that such an inversion is related to a significant importance of outgoing links in neural systems: in a sense such links transfer orders, while ingoing links bring instructions to a given neuron from other neurons. The correlator κ ¼ 0. 125 is small and thus the network structure allows one to perform a control of information flow in a more efficient way without interference of errors between orders and executions. We saw already in Sec. VII. A that such a separation of concerns emerges in the software architecture. It seems that the neural networks also adopt such a structure. We note that a somewhat similar situation appears for networks of business process management where principals of a company are located at the top CheiRank position while the top PageRank positions belong to company contacts  (Abel and Shepelyansky, 2011).
D. Networks of game go The complexity of the well-known game go is such that no computer program has been able to beat a good player, in contrast with chess where world champions have been bested by game simulators. It is partly due to the fact that the total number of possible allowed positions in go is about 10 171 , compared to, e. g. , only 10 50 for chess  (Tromp and Farnebäck, 2007). It has been argued that the complex network analysis can give useful insights for a better understanding of this game. With this aim a network, modeling the game of go, was defined by a statistical analysis of the databases of several important historical professional and amateur Japanese go tournaments  (Georgeot and Giraud, 2012). In this approach moves and nodes are defined as all possible patterns in 3 × 3 plaquettes on a go board of 19 × 19 intersections.
ACKNOWLEDGMENTS We are grateful to our colleagues  M. Abel ,  A. D. Chepeliankii ,  Y. -H. Eom ,  B. Georgeot ,  O. Giraud ,  V. Kandiah , and  O. V. Zhirov  for fruitful collaborations on the topics included in this review. We also thank our partners of the  EC FET  open project NADINE  A. Benczúr ,  N. Litvak ,  S. Vigna , and colleague  A. Kaltenbrunner  for illuminating discussions. Our special thanks go to  Debora Donato  for her insights at our initial stage of this research. Our research presented here is supported in part by the  European Commission Future and Emerging Technologies (EC FET) Open  project " New tools and algorithms for directed network analysis " (NADINE No. 288956 ). This work was granted access to the  HPC resources of CALMIP (Toulouse)  under the allocation 2012-P0110.
Role of attentional feedback and neuromodulators in learning AuGMEnT implements a four-factor learning rule. The first two factors are pre-and post-synaptic activity of the units and there are two additional "gating factors" that enable synaptic plasticity. The first gating factor is the feedback from units in the motor layer that code the selected action. These units send an attentional signal back to earlier processing levels to tag synapses responsible for selecting this action. The importance of selective attention for learning is supported by experiments in cognitive psychology. If observers select a stimulus for an action, attention invariably shifts to this stimulus  [55]  and this selective attention signal gates perceptual learning so that attended objects have larger impact on future behavior  [56] [57] [58].
Working memories in LSTM rely on the persistent activity of memory units, which resemble the ones used by AuGMEnT. However, LSTM relies on the biologically implausible error-backpropagation rule. To our knowledge, only one previous model addressed the creation of working memories with a neurobiologically inspired learning scheme, the prefrontal basal-ganglia working memory model (PBWM)  [72] , which is part of the Leabra cognitive architecture  [79, 80]. Although a detailed comparison of AuGMEnT and Leabra is beyond the scope of this article, it is useful to mention a few key differences. First, the complexity and level of detail of the Leabra/PBWM framework is greater than that of AuGMEnT. The PBWM framework uses more than ten modules, each with its own dynamics and learning rules, making formal analysis difficult.
The work was supported by grants of the  European Union  ( project 269921 ''BrainScaleS ";  PITN-GA-2011-290011 "ABC"; ERC Grant Agreement n. 339490 ) and a  NWO grants (VICI and Brain, Cognition  grant n. 433-09-208  and  EW  grant n. 612. 066. 826 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
g. , the data presents in the bank's database. Definition 4 (Double Spending Identification) An e-cash protocol ensures Double Spending Identification if there exists a test T DSI : TR × TR × D → ID ∪ {⊥} satis- fying: for any two valid transactions tr 1 and tr 2 that are different but involve the same coin ( i. e. , transId(tr 1 ) = (s 1 , c), and transId(tr 2 ) = (s 2 , c) for some coin c with s 1 = s 2 ), there exists p ∈ D such that T DSI (tr 1 , tr 2 , p) outputs (idc, e) ∈ ID × D, where e is an evidence that idc withdrew the coin c. Double Spending Identification allows the bank to identify the double spender by running a test T DSI on two different transactions that involves the same coin. For example, consider a protocol where after a successful transaction the seller gets x = m. id + r where id is the identity of the client (e.
In this case the bank makes two parallel database lookups to check if the coin was deposited before. If the parallel deposit was not finished yet and thus the coin is not yet inserted in the database, then each lookup confirms that the coin was not deposited before which results in acceptance of two spends of the same coin. This attack may be avoided with some synchronization like locking the table when a coin deposit is initiated and then unlocking it when the operation is finished. ProVerif does not support such an feature. Protocols that rely on state could be analyzed using the Tamarin Prover 4  thanks to the SAPIC 5  tool (we keep this for future work. ). Note that corrupted clients cannot create a fake coin as the correspondence holds without injectivity. Double Spending Identification and Exculpability are not relevant in the case of on-line protocols as their countermeasure against double spending is the on-line calling the bank at payment, and thus they do not have any kind of test to identify double spenders.
Payment Phase: 1. To make the payment, the client presents the pair H and the bank's signature S to the seller. The seller checks the signature, if it is correct then he chooses and sends a random binary bit y, a challenge, to the client. The client returns to the seller: • The values a and c if y is 0. • The values a ⊕ u and d if y is 1. 2. The seller checks the compliance of the values sent with the pair H. If everything (the signature and the values) is correct, the payment is accepted. At the end of the payment phase, the seller holds the pair H, the signature S, the values of either (a, c) or (a ⊕ u, d), and the challenge y. All these data together compose the transaction the seller has to present to the bank at deposit. Note, in case where n pairs are used for the coin, the challenge y will be n bit string and for each bit either the corresponding values of (a, c) or (a ⊕ u, d) are revealed to the seller.
Deposit Phase: 1. The seller contacts the bank and provides it with the transaction (H, S, y, (a, c)) or (H, S, y, (a ⊕ u, d)). 2. The bank checks the signature and also whether the values (a, c) or (a ⊕ u, d) correspond to their hash value in H. If any of these values is incorrect, the fault is on the seller's part, as he was able to independently check the regularity of the coin at payment. If the coin is correct, the bank checks its database to see whether the same coin had been used before. If it has not, the bank credits the seller's account with the appropriate amount. Otherwise, the bank rejects the transaction. Chaum off-line protocol does not prevent double spending, however it preserve client's anonymity only if he spend a coin once. Note that, a double spender can be identified when the coin has the form (h(a, c), h(a ⊕ u, d)).
MATWIN process The MATWIN platform is based on a public-private partnership bringing together the seven French Cancéropôles and 11 of the biggest pharmaceutical companies worldwide: Amgen, AstraZeneca, Bayer Healthcare, Bristol-Myers Squibb, Celgene, GSK, Merck Serono, Novartis, Pierre Fabre, Roche, and Sanofi. This nationwide program is exclusively dedicated to the maturation of early oncology research projects up to the preclinical proof of concept. Since January 2011, the program has been managed through a private society MATWIN SAS, with offices located in Bordeaux, which is now a wholly-owned subsidiary of the UNICANCER group gathering the 20 French Comprehensive Cancer Centers. Using Cancéropôles' networks, MATWIN experts identify and qualify patented French academic research projects in oncology to reinforce proof of concept and preclinical maturation before transfer to the pharmaceutical industry.
A win-win partnership Through MATWIN, all program stakeholders are committed to a win-win partnership: -By integrating the MATWIN program, the project leader benefits from project assessment (three to five international industrial and academic assessments per project), individual high value-added coaching with pharmaceutical development specialists, and the opportunity to present the project to a board that is unique in Europe with top executives from pharmaceutical companies and oncology key opinion MATWIN thus acts as a clear single window process in cancer research that contributes to accelerate innovation transfer to the benefit of patients on a major public health issue. It has long been said that France lagged behind countries like the USA regarding research public-private partnerships which have been part of the economic landscape and research culture for a long time in the USA.
5-30 nM). Nonspecific binding was determined in the presence of 10 M mianserin. Reactions were incubated for 60 min at 25 °C, and bound ligand was separated from free by vacuum filtration through GF/C filters (Brandel Inc. , Gaithersburg, MD) that had been presoaked in 50 mM Tris-HCl, pH 7. 5, 0. 5% poly(ethyleneimine) (50% solution; P3143, Sigma). The filters were washed twice with cold 50 mM Tris-HCl, pH 7. 5, and bound ligand was estimated by liquid scintillation spectrometry. Competition binding assays were carried out in a similar way but with a constant concentration of [ 3 H]mesulergine (5 nM) and the addition of a range of concentrations of ligands of interest (0. 03 nM to 10 M). Data were analyzed using GraphPad Prism version 5. 03 (GraphPad Inc. ). Calcium Mobilization Assays-Flp-In TM T-REx TM 293 cells able to express the 5-HT 2C -A206K eGFP construct in an inducible manner were seeded into poly-D-lysine-coated, black, clear bottom 96-well microtiter plates at 50,000 cells/well for use 48 h later.
24 h after construct induction with doxycycline, the cells were loaded with the calcium-sensitive dye Fura-2, by exchanging the medium for DMEM containing 3 M Fura-2AM. The plates were incubated in the dark for 45 min at 37 °C and then washed with 2 ϫ 100 l/well HEPES buffer (130 mM NaCl, 5 mM KCl, 1 mM CaCl 2 , 1 mM MgCl 2 , 20 mM HEPES, and 10 mM D-glucose, pH 7. 4). 100 l/well HEPES buffer was then added, and the plate was incubated at room temperature for 45 min in the dark. The effect of ligands was then assessed by measuring changes in Fura-2 ratio in response to calcium mobilization using a FLEX-Station (Molecular Devices, Sunnydale, CA). Data were analyzed using GraphPad Prism version 5. 03. Native Blue Polyacrylamide Gel Electrophoresis-Flp-In TM T-REx TM 293 cells induced with doxycycline to express EGFR-A206K eGFP, with or without treatment with EGF, were harvested in 1ϫ PBS and lysed in lysis buffer (150 mM NaCl, 0.