dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion of Table 1 The ability to use persistent homology and complexity of cancer PPI networks for the exploration of the importance of specific cancer-related proteins has a great potential for distinguishing "driver" mutations from those that represent "passenger" events. Because Betti numbers Table  1  Betti number analysis and its implication for single protein targeting. The Surveillance, Epidemiology and End Results Database (SEER) provide data for the 5-year survival, degree-entropy was calculated from the Protein-protein interaction network (PPI) using Python®, Cycle-Basis was calculated using networkX. cycle_basis (a Python function), the nominal Betti number was calculated using JPLEX, and represents the Betti number for the full cancer network, and the Best Betti represents the lowest Betti number achieved by removal of a protein from the specific cancer PPI network.
Methods For most cancers a great deal of molecular information is now available. The information has been for many cases compiled, curated, and posted at the Kyoto Encyclopedia of Genes and Genomes (KEGG)  [17] , and is publicly available at http://www. genome. jp/kegg/pathway. html. Similarly, detailed statistical information about the five-year survival rates of patients with cancer is readily available at the Surveillance Epidemiology and End Results (SEER) National Cancer Institute database. We have previously published the details of how the KEGG pathway data can be converted into protein-protein interaction networks  [1]. Briefly, the approach involves processing the KGML files representing the pathway network using a software program known as KEGGgraph  [18]  available at Bioconductor (http://www.
bioconductor. org/packages/ 2. 4/bioc/html/KEGGgraph. html). The R-script converts the KEGG file into an adjacency list for a protein-protein interaction (PPI) network, and using DAVID  [19]  (http:// david. abcc. ncifcrf. gov/) one can convert the Entrez IDs into gene symbols. Cytoscape  [20]  can then be used for visualization and analysis of the PPI networks. The betweenness-centrality was calculated using the Python code networkx. betweenness_centrality(G), and the betweenness-centrality values were confirmed with a Cytoscape Network Analysis plugin. To compute the persistent homology/ Betti numbers associated with the cancer networks, we used the Matlab implementation of a software package for JPlex (http:// www. swmath. org/software/9851).
A protein with a very highly up-regulated mRNA expression is assumed to be of importance in the networkthey do not actually compute network entropy. This is how many targets are presently "discovered", by assuming that the strength of up-or down-regulation of a gene reflects its importance. Our method, in contrast, analyzes each of the cancer PPI network as a unique network and avoids biasing its complexity with expression data. The shortfall of course is that the method relies on the quality of the KEGG PPI networks. However, it is extremely reassuring that despite the uncertainty associated with KEGG networks information, there is a strong correlation between network complexity and survival data from SEER. Reviewer 2: Dr. Tomasz Lipniacki Rice University, United States of America Comments on Design Principles for Cancer Therapy guided by changes in complexity of Protein-Protein Interaction Networks by Benzekry et al.
Tumor stratification by stage at diagnosis and cell type. Survival in most tumors is correlated inversely with stage at diagnosis. If deregulation of gene or protein network also depends on stage (quite likely), this will create association even if causality is absent. Therefore, stratification by stage is so important in cancer studies. SEER database includes stage information for major cancers such as for example lung cancer. In my opinion, the data analysis section should be supplemented by stratified analyses. 2. It is known that given sufficiently many parameters (or for example, many possible arrangements of mutations over the nodes of a network) it is possible to find some that correlate with outcome such as survival. Therefore, it is useful to randomly split the data set into test and validation samples.
If the test developed works satisfactorily in the validation sample, then this makes it plausible. SEER or another database will be helpful for this purpose. 3. Relationship in Fig. 2  looks much more parabolic than linear, and the interpretation of Betti index value will be quite different in this latter case. Author's response to reviewer number 3 1. We agree with the reviewer, that analysis by stage at diagnosis and cell type would have been of interest. However, even though SEER provides stage-survival data, KEGG does not. The KEGG PPI networks are an average of many known networks for each specific cancer. We discuss the characteristics and construction of KEGG networks in our response to Reviewer 1. While, as the reviewer points out survival in most tumors is correlated inversely with stage at diagnosis, and while it is likely that deregulation of gene or protein network also depends on stage, the intention was not to create associations, but rather create an understanding about PPI networks and how to measure changes occurring with removal of a node.
While stratification by stage is very important in cancers, stratified analyses may be difficult using present databases. 2. We understand the concept of leave-one/-many-out for cross validation. This is commonly done with machine learning algorithms. However, this was not a machine learning algorithm or algorithm using any kind of mapping. The method used here was straightforward evaluation of the relationship between a complexity measure of a PPI network and survival. Furthermore, KEGG does not provide any mutation information and the effect of mutation on networks could not be evaluated with KEGG. The individual cancer networks, as provided, are fixed averages across the number of subjects interrogated for the data. 3. We agree with the reviewer that a parabola could have been fitted to the sample set of cancers available in KEGG.
We have, for the purposes of this manuscript, focused on the protein-protein interaction network as generated by R-script KEGGgraph (described in the methods section). We also do not elaborate on mutation data, because mutation data are not embedded in the KEGG network, but rather link to these networks. The reviewer's example of the PML-RARA interaction is not part of the main KEGG interaction map because these are abstracted away by KEGGgraph. The real human biology is immensely complex and we are in the early stages of accumulating the content of gene-gene, proteinprotein or protein-gene interaction networks. While we share the reviewer's concern about the incompleteness of these data sets, the mutation and fusion information is not produced in KEGGgraph operation for analysis. As requested, we now include the adjacency lists of the proteinprotein interaction networks in Additional file 2: Table  S1.
Betti number analysis and its implication for multiple protein targeting. The data for the 5-year survival was again derived from Surveillance, Epidemiology and End Results Database (SEER), the nominal Betti number was calculated using JPLEX, and represents the Betti number for the full cancer network, and the Best Betti represents the lowest Betti number achieved by removal of a protein from the specific cancer protein-protein interaction (PPI) network. Through sequential removal of protein pairs across the entire PPI network of each cancer, we were able to identify combinations of protein pairs exhibiting most effect on the PPI network. Shown are the node combinations which produce the most reduction in network complexity as measured by the "Best Betti, double node elimination", each paired node elimination results in equivalent drop in Betti, e.
Acknowledgments We gratefully acknowledge the importance of the many discussions we had with  Michael La Croix , from  MIT in Boston. We appreciate the input from  Philip Hahnfeldt  at  GeneSys Research Institute (GRI) in Boston. At the time the work on this manuscript begun, SB, EAR and GLK were members of the  Center of Cancer Systems Biology, GeneSys Research Institute in Boston , and were funded in part by  U54CA149233 NCI/NIH  (to Lynn Hlatky). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health or GRI. GLK acknowledges funding from  NIH/NIGMS, Binational Science Foundation  and the  Newman Lakka Cancer Foundation. EAR is presently funded by the  Newman Lakka Cancer Foundation.
After the successful first run of the LHC, data taking is scheduled to restart in Summer 2015 with experimental conditions leading to increased data volumes and event complexity. In order to process the data generated in such scenario and exploit the multicore architectures of current CPUs, the LHC experiments have developed parallelized software for data reconstruction and simulation. However, a good fraction of their computing effort is still expected to be executed as single-core tasks. Therefore, jobs with diverse resources requirements will be distributed across the Worldwide LHC Computing Grid (WLCG), making workload scheduling a complex problem in itself. In response to this challenge, the WLCG Multicore Deployment Task Force has been created in order to coordinate the joint effort from experiments and WLCG sites.
Dynamic scheduling SGE at GRIDKA While other sites have been willing to complement their batch systems with their own scripts the goal at GridKA was to use only SGE native features to minimize the amount of resources wasted with draining and let the experiments decide how they should use their fairshare ranking and prioritizing their workload in their framework. They configured a SGE Parallel Environment for the jobs to request the appropriate amount of resources and capped the number of slots that can be drained at the time by using maxreservation. Their worker nodes are not partitioned and can run single-core and multicore. With this setup they achieved a degradation of less than 2%. Figure  7  shows both ATLAS and CMS running at GridKA. They are a good show case of how it is not possible to ignore single core jobs from other VOs at shared sites.
Acknowledgments. We thank  Julien Berestycki  for interesting discussions at the earliest stage of this work. We are also very grateful to the anonymous referees for their careful reading of this paper, which has led to a deep improvement of the presentation. This work was funded by project  MANEGE  " Modèles Aléatoires en Écologie, Génétique et Évolution" 09-BLAN-0215 of ANR (French national research agency ), Chair Modélisation Mathématique et Biodiversité VEOLIA-Ecole Polytechnique-MNHN-F. X. and the professoral chair  Jean Marjoulet. While  most of this work was carried out, the second author was affiliated with  CWI  and his research was funded by an  NWO-VIDI  grant.
The first and fourth author were supported by  EPSRC  Grant  EP/K025090/1. The research of the second author has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement n. 267959. The research of the fifth author was cofinanced by the  European Union (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: ARISTEIA II.
Photogrammetry scan Photogrammetry is based on photographs to reconstruct the shape, dimensions and position of the photographed object. A photographer performed the photogrammetry scan during the last day of the in-situ study. The scan was constituted of 354 pictures, covering a circular view of the wreck. We used the software Agisoft Photoscan to compute an alignment constituted of 115 140 points (Figure  6 ). The model is finally georeferenced with respect to a target positioned on the ground (Figure  9 ). Fig. 9. Geo-referenced mesh A lighter 3D model was produced (1,000,000 faces), from the geo-referenced model, for mediation purposes and collaboration with the virtual reality team. All the 3D models were provided in PLY format, with an associated texture. Vertexes were also colourized, allowing a visualisation without texture.
ACKNOWLEDGEMENTS This work is supported by the  French Ministry of Culture and Communication , the  French Ministry of Higher Education and Research, the regional council of Région Bretagne , and  UEB. The authors wish to warmly thank the all team of the excavation project especially  Eric Rieth  and  Catherine Lavier  (CNRS). Thanks also to  Stéphane Jupin  et  Aurélie Guesdon  (Services  3D ) for their precious help for the post project treatment based on data restitution.
Experimental Protocol Data FB15K is a subset of Freebase, a very large database of generic facts gathering more than 1. 2 billion triples and 80 million entities. Inspired by  (Hinton, 1986) , FAMILY is a database that contains triples expressing family relationships (cousin of, has ancestor, married to, parent of, related to, sibling of, uncle of) among the members of 5 families along 6 generations. This dataset is artificial and each family is organized in a layered tree structure where each layer refers to a gen-eration. Families are connected among them by marriage links between two members, randomly sampled from the same layer of different families. Interestingly on this dataset, there are obvious compositional relationships like uncle of ≈ sibling of + parent of or parent of ≈ married to + parent of, among others.
Results on quadruples While usual evaluations for link prediction in KBs focus on predicting a missing element of a test triple, we propose here to extend the evaluation to answering more complex questions, such as (h, { 1 , 2 }, ?) or (?, { 1 , 2 }, t). Examples Table  3  presents examples of predictions of both TRANSE and our model RTRANSE on such quadruples. The two first examples try to predict the origin of a TV series from the nationality of its actors. In the first one, RTRANSE is able to correctly infer the country of origin, whereas TRANSE is affected by that fact that one of its main characters, Crista Flanagan, is American of Irish descent. In the second one, given the multiculturalism of the cast, both models list the countries of the members of the series. Prediction performance For a more quantitative analysis, we have generated a new test dataset of link prediction on quadruples on FB15K.
Hybrid architectures are however only one rather direct example of how P2P intuitions might be leveraged to realize large-scale distributed systems. Skype for instance has recently moved to a cloud-based infrastructure, but nevertheless still retains many of the landmarks of its P2P past (e. g. , supernodes)  [8]. Skype's recent history exemplifies how thinking decentralized is an excellent way to achieve scalability even when the targeted infrastructure includes powerful data centers and dedicated servers. In this paper, we illustrate this connection through several examples, highlighting how the legacy of P2P is out there in many of the innovative solutions proposed to tackle the challenges of extra large, geo-replicated distributed systems. First, we discuss key-value store systems, a popular form of distributed storage underpinning many NoSQL databases (Section 2), which are a clear legacy of P2P 0" K$1 "" i" (i+K/2)"mod"K" (i+K/4)"mod"K" successor" link" (i+K/8)"mod"K" node" finger"link" Figure  1  Principle of a ring-based DHT such as Chord  [3]  Distributed Hash Tables (DHTs).
The potential downside of this strategy is the loss of extreme scalability: the size of the system is constrained by how much routing information can be stored on a single node. The approach is however reported to work well to up to a few hundred nodes, and can be extended with hybrid techniques and hierarchical designs. Riak, for instance, proposes a multi-data-center replication scheme for fault tolerance purposes, in which a source Riak instance is periodically mirrored into a sink Riak cluster. [1] http://docs. basho. com/riak/latest/, accessed 2 June 2015 A second line of extension uses specific data-structures for keys and/or values. For instance, using lists or dictionaries for values creates a flexible storage structure organized in rows by columns that is reminiscent of relational databases (although generally without any of the ACID properties relational databases usually provide).
Adding time-stamps (e. g. , as in BigTable  [17] ) or version numbers (as in Dynamo  [11] ) to values provides versioning, while adding timestamps to keys makes the data-store akin to a multi-version database. Finally, a third line of extension adds additional querying capabilities, such as range queries  [18, 19] , by combining additional routing links, and well-chosen hash functions  [20]. The main legacy of DHTs in these recent systems is the use of consistent hashing to distribute data uniformly over a large number of machines. The resulting systems, although they are designed to run in data centers on a few hundreds or thousands of machines, rather than on swarms of millions of home-based machines, remain inherently peer-to-peer in that they avoid any central component. They are also able in most cases to fall back on peer-based routing and reconciliation approaches (using mechanisms such a gossip-based anti-entropy  [21] ) to overcome failures and provide elastic growth, a crucial strength in highly dynamic cloud environments.
A brute force KNN computation has an O(N 2 ) complexity, N being the number of vertices in the graph, and designing low-complexity KNN algorithms remains an open problem. While KNN graphs have played a crucial role in many applications, they are now increasingly applied to huge databases. Consequently, as often in a Big Data context, scalability is of utmost importance. The challenge is to cope with many users and items, at acceptable costs and speeds. Traditional centralized approaches achieve this by constructing the KNN graph offline and exploiting elastic cloud platforms to massively parallelize the recommendation jobs on numerous nodes  [36, 37]. However, accounting for dynamics requires periodic recomputations which turn out to be very costly  [36, 38, 39]. Instead of deploying increasingly larger back-end servers to compute KNN graphs, alternatives have been recently proposed that exploit sampling to reduce drastically the dimension of the problem while achieving close to accurate results.
More precisely, each protocol maintains at each node two views, a data structure containing references to other nodes: the RPS view and the KNN view. Each entry in each view contains (i) the neighbor's ID, (ii) its IP address, (iii) its profile [2] , as well as (iv) a timestamp to date the last contact with this neighbor. Periodically, each protocol selects the entry in its view with the oldest timestamp  [45]  and sends it a message containing its profile with part (or all) of its view. In the RPS protocol, the peer receiving the message updates its RPS view by keeping a random sample of the union of its own RPS view and the received view. This constitutes a continuously changing random graph. In the KNN protocol, the receiving peer selects the K closest peers found in both its current KNN view, its current RPS view, and the received KNN view, i.
e. the K peers whose profiles are closest to its own according to the similarity metric. This provides a two-layer overlay network as depicted on Figure  2. Note that the KNN graph could be constructed by using the RPS view only, since the RPS protocol provides a continuously changing sample of the nodes in the system. Doing so would however be very slow, converging in O(N ) steps. The second gossip protocol, which exploits the KNN view, speeds up the convergence on the assumption that a neighbor of a neighbor in the current KNN estimation is probably a good candidate to consider for the KNN view of the local node. Crucially, the construction of the KNN graph is local (only the profile related to a peer and its neighbors are present on a given peer). There are no global data structures; instead, each peer receives for one of its neighbors a set of candidates [2] The profile of a node is application dependent and represents the interests on a peer on which the similarity with other peers will be computed.
It appears that this does not only provide scalability but also limits the influence of remote nodes on the system's stability (which might in this example actually disrupt the system). Applying such an algorithm in a cloud-based environment will yield the same benefits, with potential applications to graph processing. For instance, updating a KNN graph can be easily distributed by first partitioning the graph, and then updating only parts of it, thus limiting the need for communication between distributed units that are working on weakly connected parts of the graph. Leveraging locality User-based applications can rely on the link between users and data to create a natural form of locality. Unfortunately, this natural locality is not always present, and must instead be injected into some systems to reap the full benefits of a decentralized design.
Methods We have described the properties of optimal compensation, and given a variety of examples of optimal compensation across a range of systems. Here, we present further technical details. First, we describe how we tune a spiking network to represent signals optimally and we specify our choice of parameters for each figure. For the high-dimensional sparse coding example, we describe how we calculate sparse coding receptive fields and orientation tuning curves, both before and after neuron death. Next, we explain quadratic programming with an analytically tractable example, and we provide further details on our knock-out calculations. Finally, we prove that our spiking model performs optimal compensation. The Matlab code that we used to generate all the figures in this paper, along with all the parameters used for these figures will be published online.
Natural images are chosen because they are representative of the images that surround animals throughout evolution. We randomly select 2000 12 × 12 image patches from eight natural images taken from Van Hateren's Natural Image  Database (van Hateren and van der Schaaf, 1998). These images are preprocessed by removing low-order statistics, so that our sparse coding algorithm can more easily learn the higher-order statistical structure of natural images. First of all, images are centered so as to remove first order statistics: x → x -x. (34) Next, images are whitened, so as to remove second-order statistics: x → M -1/2 x , (35) where M = xx T is a decorrelating matrix. calculate sparse coding filters by minimizing the loss function (Equation  33 ) using a two step procedure. First of all, for a subset of 50 image patches, we calculate the firing rates that minimize the loss function under the constraint that firing rates must be positive: r = arg min r i ≥0∀i E.
Acknowledgements. We thank  Tony Movshon ,  Pedro Gonc  ¸alves, and  Wieland Brendel  for stimulating discussions and  Alfonso Renart ,  Claudia Feierstein ,  Joe Paton  and  Michael Orger  for helpful comments on the manuscript. S. D. acknowledges the  James McDonnell Foundation Award  and  EU  grants  BACS FP6-IST-027140 ,  BIND MECT-CT-20095-024831 , and  ERC   FP7-PREDSPIKE. C. K. M. acknowledges an grant of the  Deutsche Forschungsgemeinschaft  and a Chaire d'excellence of the  Agence National de la Recherche.