dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure3. Multi-omics data visualization in Cell Cycle signalling map. Five types of omics data, copy number, expression, methylation, mutations, proteomics, for breast invasive carcinoma dataset from cBioPortal has been displayed on the map using the pre-defined display mode as detailed in Table1. The values represent average for 825 samples available in the dataset (A) Top level view of data distribution, (B) and (C) Zoom in on individual entities on the map.
Figure5. Comparison of base excision repair module regulation in two cancer types. Expression and mutation data from cBioPortal has been displayed on the BER module map using the pre-defined display mode as detailed in Table1. The values represent average for all samples available in each dataset (A) BER in adenocortical carcinoma (B) Zoom in on initial steps of BER in adenocortical carcinoma, (C) Zoom in on execution step of BER in adenocortical carcinoma, (D) BER in ovarian serous cystadenocarcinoma, (E) Zoom in on initial steps of BER in ovarian serous cystadenocarcinoma, (F) Zoom in on execution step of BER in ovarian serous cystadenocarcinoma.
Data display settings in NaviComThe NaviCom web page is freely accessible via http://navi com. curie. fr. cBioFetchR and navicom codes are available at https://github. com/sysbio-curie/cBioFetchR and https:// github. com/sysbio-curie/navicom respectively. NaviCom, cBioFetchR and navicom are distributed under LGPL license. A detailed documentation including introduction into the NaviCom features, code description, manual, tutorial and application examples and suggested scenarios are available at https://navicom. curie. fr/downloads.
Acknowledgements We thank  Eric Bonnet  for technical advices and help in website generation. We thank  Maria Kondratova  for testing NaviCom. This work has been supported by the  Agilent Thought  Leader Award # 3273 ;  ASSET European Union Framework Program 7 project  under grant agreement  FP7-HEALTH-2010-259348 ; grant ' Projet Incitatif et Collaboratif Computational Systems Biology Approach for Cancer ' from  Institut Curie. This work has received support under the program «Investissements d'Avenir» launched by the  French Government  and implemented by  ANR  with the references  ANR-11-BINF-0001  (Project  ABS4NGS ) and  APLIGOOGLE program  provided by  CNRS. Funding for open access charge:  Institutional budget of institut Curie-INSERM U900 department.
IR correlates with high CpG density and low gene expression. We performed fluorescence-activated cell sorting (FACS) of primary mouse promyelocytes and granulocytes isolated from mouse bone marrow according to our established protocol (Supplementary Fig. 1a, b )  2. Our previous conservative algorithm to detect IR identified 121 introns from 86 transcripts that show differential IR in granulocytes compared to promyelocytes 2. We have now refined our previous IRFinder algorithm to better estimate IR from sequencing reads and to include more introns in our analysis. This is achieved mainly by removing biases such as low mappability and overlapping features but also using split reads as a measure of correct splicing surrounding IR (Methods section). This method allows for a more sensitive detection of IR and of differential IR.
Rapid immunoprecipitation and mass spectrometry. Rapid immunoprecipitation and MS was performed as previously reported  54  , except that the chromatin immunoprecipitation step was performed using the Magna Chip A/G kit (Millipore 17-10085). Briefly, cells were sonicated and incubated overnight with antibody of interest mixed with protein A/G magnetic beads as described in the chromatin immunoprecipitation Method section. Following washing of antibody-proteinbeads complex with buffers provided in the kit, trypsin digestion was perform to release the bound peptides. MS was performed on a QExactive Plus (Thermo Fisher). The data were searched within the Mouse SwissProt database using the Mascot search engine (version 2. 4. 0) with tryptic specificity allowing up to two missed cleavages, peptide mass tolerance of 10 p.
Besides the WGBS and RNA-seq reads generated from promyelocytes and granulocytes, we also analysed WGBS, RNA-seq and MeCP2 ChIP-seq data from the GEO (Gene expression Omnibus) database (Supplementary Table  1 ). All RNA-seq datasets have been generated from poly-A enriched libraries as published elsewhere 2,12,55-57. To assess genome-wide DNA methylation data, WGBS data was aligned to mouse genome GRCm38/mm10 and human genome GRCh37/hg19, respectively, followed by the calculation of methylation level (beta value) at each CpG site using Bismark v0. 9. 0 (ref. 50) with default parameters. Only those CpG sites covered by more than five reads were considered as valid for the downstream analysis. A sliding window of 10 bp was then applied to scan the ±100 bp region of a splice junction or from the middle of an intron.
Acknowledgements We thank  Steven Allen ,  Frank Kao ,  Suat Dervish  and  Caroline Royle  for FACS and  Ben Crossett  for MS. This work was supported by the  National Health and Medical Research Council  (Grant # 1061906  to J. E. J. R. , W. R. , # 1129901  to J. E. J. R. and J. J. -L. W. and # 1080530  to J. E. J. R. , W. R. , J. J. -L. W. ); Cure the Future (J. E. J. R. );  Agence Nationale de la Recherche  (Grant #  ANR-16-CE12-0007  to W. R. ) and an anonymous foundation (J. E. J. R. ). J. J. -L. W. and W. R. hold Fellowships from the  Cancer Institute of NSW. J. H. is a  National Breast Cancer Foundation Fellow.
Data storage on the cloud is growing every day and many companies, administrations, and individuals are now outsourcing storage of their data to large-scale Cloud Service Providers (CSP). However, because of today's cloud infrastructure virtualization, data owners cannot easily know the location where their data are stored. Even in case of the establishment of a strong Service-Level Agreement, which includes an initial guarantee regarding data location, the CSP may then move data to another location, like another country, in order to cut storage costs or for any other reasons, including backup mistakes and fraudulent use of data. Data location verification is required due to legal, privacy, and performance constraints. Recently "Where are my data located in the cloud?" has become a challenge and solutions have been proposed to verify data location, under given assumptions regarding CSP behavior.
irain@irit. fr Toulouse Institute of Computer Science Research, University of Toulouse, Toulouse, France • Software-as-a-Service (SaaS), which means that CSP provides applications (or services) to users. Well known SaaS are Trello  [5]  and Google Suite  [6]. Cloud usage is growing every day and many companies, administrations, and individuals (all of them are simply called users in the sequel) are now outsourcing storage of their data to large-scale distributed storage systems. Such users are thus relieved of tasks related to the management and maintenance of underlying storage equipment. The counterpart is that they lose some control on their data and they have to trust CSPs, thus resulting in some users remaining reluctant to cloud usage. Consequently, for the cloud to be more widely accepted, users can enforce their requirements through QoS clauses including data location.
Why data location should be known to data owners? Data collecting, storage, and processing are of prime importance in modern societies, governments, and individuals. Holding data is power whatever is the field (health, science, technology, national security, military,. ). Laws applicable to data are generally the ones of CSP's country, which may be different or worse in contradiction with the ones in data owners' country. In addition, because of today's cloud infrastructure virtualization, data owners cannot easily know locations of their data. That is why countries worldwide require compliance with their specific laws regarding data storage and processing. Objectives of data protection laws  [7] [8] [9]  are mainly: • Data sovereignty: governments must provide and enforce laws to guarantee data independence to their citizens and companies in order to avoid particularly data access deny by foreign authorities.
Indeed, in case of conflicts between data owners' country and CSP's country, the latter may totally or partially deny data access. • Data protection: any government must protect data in case of war or any critical conditions, i. e. governments must protect citizens and societies as well as their data and do not rely on other countries to provide the required protection. • Privacy protection: data belonging to citizens and strategic companies must be protected against accesses from abroad. For example, national companies should not store their data in other countries where local governments could redistribute stored data to local competitors. In addition to data protection laws, data location may be required in order to consider performance issues  [10] : the closer to users are the data, the faster is the access.
Why data location verification is needed? When a contract is agreed between data owner and CSP, the latter is assumed to comply with contract rules. For example, the following is posted on AWS's webpage: "Customers choose the region(s) in which their customer content will be stored. We will not move or replicate customer content outside of the customer's chosen region(s), except as legally required and as necessary to maintain the AWS services and provide them to our customers and their end users"  [11]. However, even in case of the establishment of an SLA, which includes an initial guarantee requirements regarding data location, CSP may move data to another location, like another country, in order to cut costs, by mistake or for malicious reason. For example, in November 2016, Russia's communications regulator ordered public access to LinkedIn's website to be blocked to comply with a court ruling that found the social networking firm guilty of violating a data storage law, which stipulates the personal data of Russian users must be stored on the territory of the Russian Federation  [12].
Data location verification categories Recently "Where are my data located in the cloud?" has become a challenge and some authors proposed approaches allowing users to verify data location under given assumptions regarding the CSP behavior, connection links between users and the CSP, and so on. Those solutions and associated assumptions are investigated in the sequel. Three location verification approaches classes are commonly distinguished (Fig. 1 ): • Cloud Framework-based approaches  [15] [16] [17]  aim at providing a software framework to CSP. The latter must use this framework, meaning that virtualization management is done by the provided framework. The latter replaces the software stack usually used by CSP. Provided framework allows users to specify different policies regarding their data.
Solution To avoid VM misappropriation by a malicious CSP, the simplest way is not to use VM to implement location verifier. Rather, location verifier should be hosted on a private machine owned by the user or on a trusted third party. However, if for any good reasons regarding user's preferences or requirements, location verifier is hosted by the CSP, the user must deploy on the CSP a trusted hardware, such as a Trusted Platform Module, to prevent CSP to manipulate the hosted VMs  [29].
Attack principle CSP is aware of the user's behavior, which shows that the user relies on RTT measured during data access to derive CSP location. CSP chooses a node at location X, using an appropriate high-speed network, such that delay access between CSP and the chosen node is negligible compared to the variation of RTT between the CSP and user. CSP stores data at the location X. Then, when the user sends data access requests to CSP, the latter reads data from location X and then forwards them to the user. It is worth noticing that this attack may also be used by a malicious proxy even though a PDP is deployed (see Malicious proxy attack).
Approaches in  [21, 26]  avoid such an attack owing to PDP protocol utilization. In  [23] , landmarks are passive and location verification is hosted on a VM of the CSP with trusted location, consequently, it has capabilities to avoid proxy attack. • Approaches in  [10, 23]  are vulnerable to VM misappropriation, because they are partially (in  [10] ) or totally (in  [23] ) VM-dependent. • All approaches are vulnerable to RTT forgery, to data caching attack-except  [21, 26] , owing to PDP protocol-, and to concealed connection attack. In conclusion, reviewed location verification approaches are (very) far from being robust to prevent malicious CSP's attacks.
Proofs of data possession It is worth noticing that without guarantees that any server replying to probing requests is the right one, collected RTT measurements are useless to location verification process. As shown in Table  1 , only two solutions include Proof of Data Possession protocols. It is surprising that PDP protocols are not prevalent in existing approaches. As already outlined, PDP protocols provide powerful mechanisms to trust collected measurements regarding RTT. One challenge would be an analysis of PDP cost regarding frequency of PDP checks. In particular, adequately increasing frequency of PDP checks makes it economically unprofitable for a malicious CSP to move data between two PDP checks. Indeed, a malicious CSP, which is aware of the period between two successive PDP checks, when this period is very high-e.
Then, the criteria guide identification of potential attacks that a malicious CSP might implement to jeopardize the location verification process. Malicious CSP relocates data on data servers, which are not authorized by users, and then tries to make them believe their data are on the location agreed in SLA. We briefly describe some solutions to overcome attacks or detect them. Finally, we discuss limitations and drawbacks of existing solutions and identity some challenges that require further investigation in the future. Location verification still remains an open and exciting issue in the cloud computing research field.
More recently, the works  [16]  and  [23]  by Xu et al. have shown that CNN-based steganalysis remains competitive with conventional steganalysis. In  [16]  they first proposed a structural design of CNNs for steganalysis that is neither large, nor deep, and learns from noise residuals, since they considered as input image the one issued from high-pass filtering using the kernel F 0. The architecture of such convolutional networks, which is the basis of our work presented thereafter, will be described in detail in the next section. The experiments they completed have considered two spatial content-adaptive steganographic algorithms: S-UNIWARD and HILL. They have shown that the performance gained by an ensemble of five CNNs is comparable to the one of SRM+EC. In fact, they trained 5 CNNs because the dataset was split into 5 test subsets, and thus an input image class was predicted by averaging the output probabilities of the CNNs.
Therefore they propose to start by manually applying to the input image the first two phases of DCTR  [8] , namely a convolution followed by Quantization & Truncation (Q&T). They use 25 residual images, where each is obtained by using a 5 × 5 DCT basis pattern, and three Q&T combinations. Then, for each group of residual maps for a given Q&T combination, a subnetwork corresponding to a simplified version of the convolutional part proposed by Xu et al. in  [16]  is trained to produce a feature vector of 512 components. To obtain the final prediction, the three vectors are concatenated and given as input to a three-layer fully connected network, which is trained together with the three subnets. Based on the experiments performed on more or less large databases of images issued from ImageNet, the authors claim that their proposal outperforms all other existing steganalysis approaches (no matter whether they are deep learning or conventional ones).
B. Detection performance evaluation of the CNN To study and assess the performance of Xu et al. proposal, which was originally evaluated using a modified version of Caffe toolbox, the corresponding CNN has been implemented with the open source software library TensorFlow  [29]. The implementation is available on download from GitHub 1. We had previously used in  [22]  the computing platform Torch  [30] , but TensorFlow seems to make better use of the GPUs. All the experiments are performed on a NVIDIA Tesla Titan X GPU, using as cover database the well-known BOSSBase  [31]  which consists of 10,000 grayscale images having a size of 512 × 512 pixels. Six stego images are associated to each cover image. They are obtained by embedding a message with S-UNIWARD, MiPOD, and HILL algorithms, thanks to the Matlab scripts downloadable from Binghamton DDE Lab website, considering two different payload values: 0.
5, 1 otherwise. (4) The aggregation of these results must take into consideration the fact that an image I we want to classify is used in training   i∈T I is stego(I, CNN i ) < 0. 5, 1 otherwise. (5) Indeed, as both training and testing sets are built by randomly picking images, the number of times an image I is in a test set varies (being at most equal to T ). Furthermore, due to the huge computation cost we have only trained CNNs using MiPOD dataset and tested them directly on the S-UNIWARD and HILL datasets. Hence, we can also assess whether a CNN trained with an embedding scheme is still competitive for the other ones. We have chosen MiPOD because it is supposed to have the best security and thus should be the most difficult to detect, even if MiPOD and HILL are very close according to  [1].
Third, for MiPOD the CNN approach is still competitive with SRM+EC. Fourth, the CNNs trained by only making use of the MiPOD dataset can provide a similar detection accuracy for S-UNIWARD and HILL. Obviously, the lowest detection error is gained for the embedding scheme which has provided the training data. Fifth, CNNs outperform SRM+EC in blind steganalysis context, which means that CNNs allow a better generalization to different steganographic algorithms. We are then left to study for which images the CNN proposal by Xu et al. fails.
A closer look on the performances of each steganalyzer on the subset of images it has to classify according to ρ ∩ U explains why our proposal is relevant. Indeed, in comparison with the performances shown in Table  I  we can remark that the SRM+EC error rate is slightly worse than on the whole dataset. Thus, we take advantage from the low error rate of the CNN at a price of a slightly worse misclassification by SRM+EC. Another point to notice is the evolution in opposite directions of ρ ∩ U and payload values, which means that, as expected, the scatterness of the modified pixels increases and thus is more difficult to detect with the current CNN architecture. Nevertheless, our approach allows us to build a competitive blind steganalyzer, which gives lower detection errors than CNN based only or SRM+EC based only approaches.
However, to be able to train a CNN for low ρ U values, the database should be expanded in order to include more images corresponding to this case of study. Second, CNNs dealing with spatial domain steganalysis work on a single high-pass filtered version of the input image, but since this filtering mainly highlights edges it does not give a relevant information to the CNN when the embedding is not edge-based. Therefore, we plan to replace the single filter by a filter bank, an approach which in the case of the JPEG domain steganalysis seems to be successful according to  [27].
5 % cover year -1 ). Trees and shrubs exhibited similar mean encroachment rates (0. 62 and 0. 52 % cover year -1 , respectively). Rates of increase for Great Plains species representing contrasting plant functional types (e. g. , evergreen vs. deciduous; N 2 fixation potential) and dispersal mechanisms were comparable as well. (continued) 1 A database of peer-reviewed research papers was compiled by searching for the terms "bush encroach*," "brush encroach*," "desertification," "shrub grazing," "shrub encroach," "shrub invasion," "shrub expansion," "woody encroach*," and "woody plant invasion" on the ISI Web of Knowledge. This search produced 865 unique references that were then subdivided into papers that quantified encroachment rates  (n = 289)  or relationships between shrub encroachment and grazing (n = 149).
2008). Some woody encroachers can generate positive density dependence by facilitating the encroachment of other woody species. In these instances, the initial encroaching Year Fig. 2. 6 Canopy cover of three shrub species and "all other shrubs" at a Sonoran Desert grassland (USA) where woody plant encroachment has been well documented. Prosopis velutina cover increased markedly from the 1950s to the 1990s, whereas that of Acacia greggii and Parkinsonia florida (both potentially N 2 -fixing) and all other shrubs has remained low (note break in y-axis). Data are from the Santa Rita Experimental Range Digital Database, Pasture 8 (http://ag. arizona. edu/SRER/longterm/ltcover. xls) species, perhaps arriving via dispersal from wind, water, ungulates, rodents, or ants, adds vertical structure to the grassland community and modifies soils and microclimate subsequent to its establishment.
Watson and Novelly (2012)  used an extensive, long-term monitoring dataset from Western Australia to determine how often predefined thresholds were crossed. During a 17-year evaluation period 11 % of grassland sites and 1 % of shrubland sites were judged to have undergone a transition. More recently, a study in semiarid wetlands in Australia provided a robust approach for quantifying the causes of state transitions and using logistic models to generate future transition scenarios  (Bino et al. 2015). While there has been a range of quantitative approaches tested, a consistent, structured approach to defining and testing for state transitions is still lacking. Following the initial interest in the STM approach and continued, sporadic development of models for different plant communities (e.
ESDs are documents that had long served as the site-specific basis for management recommendations by federal land management agencies. ESDs are linked to soil survey databases through the connection of ecological sites to soil maps maintained by the NRCS. Application of the range condition model via ESDs involved the calculation of plant community similarity between an observed and a single, historical climax plant community identified for each ecological site  (Dyksterhuis 1949). In order for the rangeland condition model to be replaced, thousands of STMs would have to be developed for ecological sites across the USA, each requiring the description of multiple plant communities.
Current Applications In many ways, the relatively recent Mongolian experience with STMs takes advantage of what was learned in the early development efforts of Australia, Argentina, and the USA. STM development began in concert in 2008 with the development of a standard methodology for vegetation measurement, based on procedures used by US government agencies  (Herrick et al. 2005 ). These procedures were officially adopted by the Mongolian government in 2011. In addition to providing a sound basis for reporting trends in rangeland vegetation, adoption of a unified measurement method ensured that cover and production values reported in STMs were comparable to monitoring data produced by the National Agency for Meteorology and Environmental Monitoring (NAMEM). Training for a GG research team on methods to develop STMs and database management began in the USA in early 2009, followed by data collection co-occurring with training in Mongolia from 2009 to 2014.