dataesr/scientific-paragraphs-categorization huggingface dataset:
It also has the capability to use expert knowledge, and is well suited for parallelization. However, the computational complexity of the CES strategy precludes a direct analysis of GWAS data with hundreds of thousands SNPs. Such datasets will require a preprocessing step with filtering methods introduced in Section Two-stage Approach: Filters to Obtain Reduced Search Space. Runtimes were not always available (NA) and are indicated for simulated datasets when no real data application is available. The notation "WTCCC-multiple diseases" stands for "WTCCC-Bipolar Disorder (BD), Coronary Artery Disease (CAD), Crohn's Disease (CD), Hypertension (HT), Rheumatoid Arthritis (RA), Type 1 Diabetes (T1D), and Type 2 Diabetes (T2D).
Regarding data integration approaches, biological expert knowledge based-filters are often proposed to guide epistasis analysis. Being a biased approach, it is recommended to run at the same time a procedure without any a priori knowledge  (Ritchie, 2015). Although development of epistasis detection methods is growing, many methods are hampered in presence of genetic heterogeneity or incomplete penetrance. Random forest-based techniques have been described to efficiently deal with genetic heterogeneity because data is split in different subsets in early stages of the algorithm  (Koo et al. , 2013). Besides, some of the existing software programs, like BEAM, will soon become unsuitable to GWAS datasets which will keep growing in size so that several millions of SNPs will be the rule rather than the exception.
On the other hand, such a huge number of SNP might increase power of existing strategies tailored to handle massive datasets. An interesting fact rarely discussed in literature describing the strategies reviewed in this survey is the confusing boundary between epistasis and linkage disequilibrium. Because linkage disequilibrium is by definition a phenomenon involving dependence between genetic variants, its frontier with epistatic interactions may be blurred since the aforementioned software programs are designed to detect SNPs that jointly affect the phenotype. This issue is particularly acute for case-only approaches. For standard case/control studies, if estimation of linkage disequilibrium within controls provides the same result as within cases, then the observed linkage disequilibrium does not originate from epistatic interactions.
Acknowledgments CN is supported by the  Regional Bioinformatics Research  project  GRIOTE  granted by the  Pays de la Loire  region on the one hand, and the  European Genomic Institute for Diabetes (EGID) Labex (Lille)  on the other hand. GR's work is supported by a Chair in Biostatistics jointly sponsored by the  Centre National de la Recherche Scientifique  and  Lille 2 University. We also thank two anonymous reviewers for very helpful comments and valuable improvement of the manuscript.
Protein-protein interaction listings, and their associated evidence strength scores, are obtained from the Mentha interactome server, or power-users may upload a pre-made network produced using some other interactomics tool. The Cytoscape. js JavaScript library is used in producing the graphical display. Importantly, for a protein that has been observed at multiple subcellular locations, users may prioritize the visual display of locations that are of special relevance to their research domain.
INTRODUCTION In the analysis of omics data, a researcher is often confronted with a short list of genes and, by extension, their encoded proteins. This list may simply contain differentially expressed genes from a single experimental comparison, or it may result from some secondary analysis, such as functional enrichment using a tool such as DAVID  (1) , leading edge analysis of Gene Set enrichments  (2) , clustering of transcripts based on the correlation of their expression profiles  (3)  or network clustering based on gene functional associations  (4, 5). More generally, based on specialist knowledge, genes/proteins may be listed based on their special interest to a particular research project. In any case, to interpret such a list and to formulate mechanistic hypotheses, it is useful to explore previously published data concerning two areas of functional context: (i) subcellular locations at which the proteins have been reported and (ii) interactions between proteins, both within the list and with other proteins outside the list.
Subcellular localization Owing to the annotation efforts of model organism databases, high-quality subcellular localization information for the proteins of many organisms can be obtained from two carefully curated sources: UniProt  (6)  and the Gene Ontology (GO)  (7). UniProt stores this information in its 'Subcellular location' field for each protein, while GO annotates proteins to the Cellular Component branch of its ontology. In both annotation systems, terms may vary from low specificity (e. g. 'Membrane') to higher specificity (e. g. 'Gap junction'), and a given protein may be annotated to multiple terms. As of writing, some 1283 terms are in use by UniProt, and 3812 by GO. Terms are fewer in UniProt because they are applied conservatively: in general only the more classically recognized location(s) of a given protein are noted, whereas GO is structured toward a systematic listing of all of the known (published) locations of a protein, even those that are rarely observed.
METHODS: NETWORK GENERATION, PROTEIN LO-CALIZATION AND GRAPH ORGANIZATION UniProt compiles a downloadable data file that includes both UniProt and GO localizations for all manually annotated (i. e. Swiss-Prot) and non-redundant protein sequences. Mentha maintains a file listing protein interactions. CellWhere downloads these files automatically within 24 h of each UniProt or Mentha update. Identifiers, localizations and interactions are then parsed and organized together with mapping information in a relational database. A CellWhere query begins by first mapping submitted gene symbols or other identifiers to the Swiss-Prot accession of the corresponding protein. Mentha data are queried to obtain (i) evidence scores for interactions between proteins of the query list and (ii) proteins that interact with the query list, selected based on the strength of the evidence score.
Spatial relation mappings are provided, but may also be set by the user. The Cytoscape. js JavaScript library (http://cytoscape. github. io/cytoscape. js/) is used to produce the graphical display in an html format that is readable by all common web browsers. Cytoscape. js was chosen over other graphing platforms--such as Cytoscape Web  (16) , D3 (http://d3js. org/) or sigmajs (http://sigmajs. org/)--in large part because of its built-in support for compound nodes (used by Cell-Where to group proteins into their localizations), but also its shared philosophy with the Cytoscape desktop application.
RESULTS: COMPARISON WITH RELATED TOOLS A general-purpose network manipulation and analysis tool such as Cytoscape can facilitate the integration and visualization of many types of information, including to group or color nodes according to subcellular localizations, but localization information must first be obtained and summarized. Further work is then required if the user desires to organize the graphical layout based on these localizations, such that the network resembles a schematic of the cell. Besides Cytoscape, there are several popular free-to-access tools that are focused on biological network analysis and/or visualization (some of which are listed above), but these tools generally lack the automatic integration of subcellular localization information. However, the localization-related functionalities of CellWhere have limited overlap with two existing pieces of software, one publicly available (Cerebral viewer  (17) ) and the other commercial (Ingenuity IPA (QI-AGEN Redwood City, www.
qiagen. com/ingenuity)). Cerebral viewer is a plug-in currently only available for the older version 2. 8 of the Cytoscape desktop application. It facilitates a stratified graph layout based on localizations, but does not provide localization or interaction information, which must be provided by the user (discussed further in Supplementary note S1). Ingenuity IPA is a data integration and exploration tool for omics data analysis. Provided as part of its networkbased clustering approach is a graph output in which genes are positioned into stratified localizations. IPA identifies interactions and localizations using a proprietary knowledgebase derived from Ingenuity's in-house literature curation. To highlight the similarities and differences between IPA and CellWhere, we re-analyzed in CellWhere a previously published  (18)  network that was produced using IPA (Supplementary note S1).
IntFOLD is an independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance. The only required input to the server is an amino acid sequence for the target protein. Here we describe major performance and user interface updates to the server, which comprises an integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality assessment, disorder prediction, structural domain prediction, function prediction and modelling of protein-ligand interactions.
INTRODUCTION The IntFOLD server is an integrated resource for modelling the structures and functions of proteins from sequences. Here we describe the significant major updates to the Int-FOLD server, which was originally published in the NAR Web Server 2011 issue  (1). The server has been operational since 2010, it has been used extensively by thousands of researchers around the world and processed tens of thousands of unique sequences. The server comprises an integrated suite of five novel methods: IntFOLD3-TS, for ter-tiary structure prediction; ModFOLD5, for model quality assessment; DISOclust3, for disorder prediction; Dom-FOLD3, for domain prediction and FunFOLD3, for function and ligand binding site prediction. The component methods within the IntFOLD server form a single sequence-structure-function annotation pipeline, with the data output from one algorithm forming the input data for another.
Such integration increases the efficiency of computation and server management, and reduces the time researchers will have to spend submitting predictions and collating and interpreting their results. Alternative servers are available that generate results using related individual methods, however, the IntFOLD server is perhaps unique in providing an integrated pipeline of multiple methods with class leading model quality assessment algorithms built-in. In addition, the server provides a single results page with unified graphical output and a single point of entry for job submission, allowing ease of access to non-experts. Furthermore, the server provides links to all of the raw machine readable output data in the standard CASP formats, for use by expert developers and independent assessors.
Server inputs and outputs Inputs. The only required input to the server is an amino acid sequence for the target protein. However, users may optionally provide a name for their protein sequence, their email address and alternative 3D models of their protein, for direct comparison and quality assessment. Graphical outputs. The output is presented as a simple table that summarizes all prediction data graphically through thumbnail images of plots and annotated 3D models (Figure  1A ). A detailed help page is provided, which serves as a guide for interpreting results with links to several example results pages. The help page also includes information on the input data formatting and detailed descriptions of the output data from the server. Users can click through the images in the table in order to drill down into individual results, download the 3D coordinates of models in PDB format and/or directly view them interactively in 3D using the new JSmol/HTML5 framework (http://www.
Parameter Prediction The study by  Stützle et al. (2012)  provides an overview of existing parameter prediction/tuning approaches for ACO in two major directions: (1) parameter choosing before running the algorithm (offline tuning) and (2) adaptation during runtime (online tuning). Offline tuning is conventionally done using trial and error methods, which is error prone, and even any good results achieved cannot necessarily be reproduced. Later on, some studies have been done using an EA and local search for offline parameter tuning  (Pilat and White, 2002). For example, the irace package  (López-Ibáñez et al. , 2011)  is an automatic algorithm configuration package based on the iterative racing and ranking. Some recent work in multi-objective ant algorithms (López-Ibáñez and Stützle, 2012) used these techniques, claiming to outperform conventional offline tuning methods.
Second, micro-kernel based approach, such as SeL4  [16] , tries to reduce the trusted computer base (TCB) by running a limited code in the privileged mode. This approach requires a redesign of operating systems, thereby requiring nontrivial modifications to port existing applications. Third, the dual-execution-environment approach (dual-EE), such as TLR  [22] , solves the problem by multiplexing the feature-rich OS and a specialized OS with restricted functionalities on the same smart device. It relies on the specialized OS to provide tamper-resistant capabilities. Applications that demand tamper-resistant protection run only on the specialized trustworthy OS. Compared to other approaches, the dual-EE is considered as a promising approach intended for practical use  [10]. The literature is full of proposals  [2, 10, 13, 17, 27].
In para-virtualization, OS needs to be modified in order to run on the underlying hypervisor. In dual-EE, the hypervisor plays the role of the SK layer, and the number of virtual machines is limited to two; 3. Isolation based on special processor extensions: this category consists in enhancing general-purpose processors with new hardware extensions. These newly-introduced extensions allow the execution of secure code within a potentially compromised OS. The most prevalent secure extensions targeting smart devices is ARM TrustZone  [4]. In this paper, we only consider ARM TrustZone because, to the best of our knowledge, it is the most deployed security extensions in practice. A processor with TrustZone extensions provides a special form of virtualization. It enables two virtual processors with two security domains: the "secure" zone and the "normal" zone.
Bare-Metal Hypervisor: KVM/ARM KVM/ARM is the ARM hypervisor in the mainline Linux kernel  [7]. It is the first hypervisor to leverage ARM hardware virtualization support to run unmodified operating systems on ARM hardware. It builds on KVM and leverages existing infrastructure in the Linux kernel. KVM/ARM is a hosted bare-metal hypervisor, where the hypervisor is integrated with a host kernel. It runs the hypervisor in normal privileged CPU modes to leverage existing OS mechanisms without modification, while at the same time leveraging ARM hardware virtualization. In contrast to standalone bare-metal hypervisors (e. g. Xen), it supports a wide range of ARM devices despite the fact that there is no standard hardware in the ARM world. In dual-EE, the two execution environments (GPEE and SEE) are two virtual machines running on the underlying hypervisor.
However, TrustZone is often completed with additional features, such as secure boot and root of trust (RoT) hardware module, which allow TrustZone to satisfy all the requirements of a tamper-resistant environment. Regarding the SK layer, it is mainly implemented in hardware. The software components to be trusted are minimal, hence evaluable. For instance, most CPU registers are banked. Thus, saving and restoring CPU registers are performed by the processor. In addition, TrustZone enables the co-existence of cache entries of both SEE and GPEE. Thus, cleaning the cache memory during a context switch is not required. As a result, the sanitization process performed during a context switch is both fast and secure, since it is almost done by the hardware. Data flow is well controlled. To enter the secure world, only a well-defined set of interfaces exist.
For example, the widely-used Qualcomm Snapdragon MSM8974 and APQ8084 processors do not implement the hypervisor extension. On the contrary, external hardware modules achieve the highest score in terms of security. Our results are expected, as these modules provide a confined execution environment which protects the application's authenticity, integrity and privacy against even sophisticated physical attacks. Nevertheless, external hardware modules do not fit to a certain kind of secure applications that need user interaction and better processing speed. As for ARM TrustZone, it comes close to perfect score. Our results are consistent and expected because TrustZone implements all the hardware extensions that the SK layer requires in order to work properly. TrustZone provides a balanced trade-off between bare-metal hypervisors and external hardware modules.
Indeed, it does not resist against some physical attacks and it requires a part of the SK to be implemented in software  [4]. In addition, TrustZone does not provide sealed storage and attestation without additional hardware modules. However, it is more secure than solutions based on bare-metal hypervisors and more flexible than those based on external hardware modules. Our framework shows that TrustZone-based solutions are efficient for real contexts. Once again, our results are consistent with existing work. At present, millions of devices integrate TrustZone-based technologies. Examples are ObC in Lumia phones  [17] , TIMA/TZ-RKP in Samsung smartphones  [6] , and <t-base of Trustonic  [2].
While the origin of language remains a somewhat mysterious process, understanding how human language takes specific forms appears to be accessible by the experimental method. Languages, despite their wide variety, display obvious regularities. In this paper, we attempt to derive some properties of phonological systems (the sound systems for human languages) from speech communication principles. We introduce a model of the cognitive architecture of a communicating agent, called COSMO (for "Communicating about Objects using Sensory-Motor Operations') that allows a probabilistic expression of the main theoretical trends found in the speech production and perception literature. This enables a computational comparison of these theoretical trends, which helps us to identify the conditions that favor the emergence of linguistic codes.
In Section 4, we define "deictic games" as the key tool for our simulations, in which computational agents interact in the presence of objects that they attempt to designate by the voice. Our second contribution, in Section 5, is an experimental study involving deictic games in a simplified sensory-motor space, of our main theoretical proposals about speech communication. It enables us to extract some cognitive conditions necessary for the emergence of a speech code in COSMO. Our third and final contribution, in Section 6, shows how the model is able to predict coherent vowel, consonant and syllabic phonological systems by using a realistic vocal-tract model and a phylogenetically and ontogenetically plausible control framework. We analyze our simulation results with respect to some of the regularities in the sound systems for human languages described previously.
Vowels Vowels can be characterized by their formant values. The debate between local parametric representations based on formants and global spectral representations, based on e. g. critical band spectra or cepstral parameters, is classical (see a discussion in  Schwartz, Boë, Vallée, & Abry, 1997a). While global parameters are the only adequate way to deal with automatic processing of acoustic stimuli, formants remain the reference in simulations about phonological inventories, therefore they will be used in the present work. Vowel system inventories available in the phonological database UPSID, which samples about 10% of human languages  (Maddieson, 1984; Maddieson & Precoda, 1989) , display clear regularities  (Maddieson & Precoda, 1989; Vallée, 1994; Schwartz et al. , 1997b). In particular, almost every language has /a,i,u/, with /a/ as in "car" (from a low jaw and a low and central tongue), /i/ as in "bee" (from a high jaw and a high and front tongue) and /u/ as in "food" (from a high jaw and a high and back tongue).
COSMO and the "internalization" hypothesis We now present the COSMO model for speech communication and language emergence. The COSMO acronym also represents the five variables around which the basic structure of the model is built (see Fig. 3 ). In COSMO, communication (C) is a success when an object O S in the speaker's mind is transferred, via sensory and motor mechanisms S and M, to the listener's mind, where it is correctly recovered as O L. The central hypothesis of the COSMO model is that a communicating agent, which is both a speaker and a listener, is able to internalize fully the communication situation described previously (see Fig. 3 ) inside an internal model (see Fig. 4 ). Firstly, the agent can take both roles, listener and speaker, and therefore contains both motor and sensory subsystems.
From a conceptual model to a computational model The first step in our simulation program is to propose a computational implementation of COSMO sensory-motor agents. This is done in the Bayesian programming framework. Applying Bayesian modeling, particularly when dealing with sensory-motor behaviors, finds its justification in the fact that any model of a real phenomenon is by nature incomplete  (Lebeltel, Bessiere, Diard, & Mazer, 2004) : there are always hidden variables that influence the phenomenon and are however not taken into account in the model. Thus, the use of classical logic quickly finds its limits and  Jaynes (2003)  proposed the use of probability theory as an alternative to logic for reasoning about incomplete and uncertain knowledge. In this framework, the subjective interpretation of probability theory allows the programmer to specify in a rigorous and precise way incomplete and uncertain knowledge of the sensory-motor agent, to propose learning and interaction processes and to simulate them by automated processes of probabilistic inference.
Fig. 5  illustrates a deictic game. Let us define N as the number of agents in the society, Q as the number of objects on which the agents can share attention and TransMS : M→S as the articulatory-to-auditory transformation. That is, each time an agent produces an articulatory configuration m∈M, both agents involved in the deictic game will perceive the corresponding auditory stimulus s ¼ TransMSðmÞ∈S. The COSMO Bayesian model of Eq. (  1 ) is embedded in each agent, with no prior knowledge in the motor and auditory subsystems. That is, the PðM j O S Þ and PðO L j SÞ distributions are largely uncertain, being, for example, uniform or Gaussian with a large variance. These distributions will be refined by learning from the data collected during their interactions. In our simulations, all agents in the society will embed the same implementation of COSMO; namely, just one of the motor, auditory or sensory-motor theories (defined in Section 3.