dataesr/scientific-paragraphs-categorization huggingface dataset:
The robot continues to randomly select possible actions and execute them, and the user provides the corresponding command, thus populating the database with <sentence, meaning> pairs. In testing mode, the system uses the data generated in the learning mode in order to fully interact with the human, whereas in the training mode the system is more passive. In the Action Performing task the human says a command to the robot (providing the sentence). This test sample is passed to the neural model (Figure  3 ). The neural model produces the corresponding meaning, which is sent back to the Supervisor which translates the meaning into the corresponding robot command(s). The robot then produces the desired action(s).
TRIALS FOR SCENE DESCRIPTION TASK For the Scene Describer task, the user was first asked to set the initial position of the objects (this is indicated by the robot when it says "set objects"). Then the user was asked to identify the focus object (indicated by "focus"). The latter is what should be in focus in the sentence, i. e. , the subject (i. e. , the agent in this case) of the sentence. After that, the robot asked the user if it should produce a usual (i. e. , canonical) or non-canonical sentence. Then the robot processed all the information and described the scene by producing a sentence ("robot sentence"). See Table  4  to see a subset of trials for the Scene Describer task. In order to get an appreciation for the near real-time behavior of the system, we examined experimental data logs and collected data from 22 experiments with the scene describer and from 66 experiments with the action performer.
In the reverse sense, the output of the comprehension model could be input to the production model, allowing the listener to predict the upcoming words of the speaker. Another alternative would be to combine both comprehension on production within a same model, with feedback connections from both discovered thematic roles and produced words: a unique reservoir would do both tasks at the same time; this would probably require an online learning algorithm. We collected data from 66 successful runs of Experiment 1 (action performer) and 22 successful runs of Experiment 2 (scene describer), with experienced users, in order to estimate the timing of the interactions. We did not focus on error analyses, but these data already indicate that the system functions reliably. The next step was to see how this would translate sentences with variability inherent to "naïve" users.
The EUMSSI project (Event Understanding through Multimodal Social Stream Interpretation) aims at developing technologies for aggregating data presented as unstructured information in sources of very different nature. The multimodal analytics will help organize, classify and cluster cross-media streams, by enriching its associated metadata in an interactive manner, so that the data resulting from analysing one media helps reinforce the aggregation of information from other media, in a cross-modal semantic representation framework. Once all the available descriptive information has been collected, an interpretation component will dynamically reason over the semantic representation in order to derive implicit knowledge. Finally the enriched information will be fed to a hybrid recommendation system, which will be at the basis of two well-motivated use-cases.
Both the journalist and the TV viewer would greatly benefit from a system capable of automatically analysing and interpreting unstructured multimedia data stream and its social background, and, with this understanding, be able of contextualising the data, and contributing with new, related information. The FP7-ICT-2013-10 STREP project EUMSSI, which started in December 2013, is developing methodologies and techniques for identifying and aggregating data presented as unstructured information in sources of very different nature  (video, image, audio, speech, text and social context) , including both online (e. g. , YouTube) and traditional media (e. g. audiovisual repositories), and for dealing with information of very different degrees of granularity. This will be accomplished thanks to the integration in a UIMA-based 1  multimodal platform of stateof-the-art information extraction and analysis techniques from the different fields involved (image, audio, text and social media analysis).
The multimodal interpretation platform, in an optimized process chain, will analyze a vast amount of multimedia content, aggregate all the resulting information and semantically enrich it with additional metadata layers. The resulting system will be potentially useful for any application in need of cross-media data analysis and interpretation, such as intelligent content management, recommendation, real time event tracking, content filtering, etc. In particular, the EUMSSI project will use the semantically enriched information to make personalized content-based recommendation.
Multimodal analytics and Semantic Enrichment For reasoning with and about the multimedia data, the EUMSSI platform needs to recognize entities, such as actors, places, topics, dates and genres. A core idea is that the process of integrating information coming from different media sources is carried out in an interactive manner, so that the metadata resulting from analyzing one media helps reinforce the aggregation of information from other media. For example, the quality of speech recognition heavily depends on the audio quality and background noise. Existing text, tags and other metadata will be exploited for disambiguation. Further, OCR on video data, speech analysis and speaker recognition mutually reinforce one another. The combined and integrated results of the audio, video and text analysis will significantly enhance the existing metadata, which can be used for retrieval and recommendation.
, 2010). Moreover,  (Hahn et al. , 2010)  have shown that the structured information gathered from Wikipedia infoboxes can be used to answer complex questions, like "Which Rivers flow into the Rhine and are longer than 50 kilometers?" For this purpose, text documents need to be previously annotated using DBpedia Spotlight  (Mendes et al. , 2011) , which automatically annotates text with links to articles in Wikipedia. The process of semantic enrichment is still largely domain-dependent; therefore, apart from the available general-purpose knowledge bases and ontologies (DBpedia, FOAF, DublinCore. ), the EUMSSI platform needs specialized resources for categorizing videos on different dimensions. Linked Data technologies  (Heath and Bizer, 2011)  and the Linked Open Data cloud 2  provide access to several of these resources, including geodata, movie databases and program information.
Architecture overview All new content coming into the system is first normalized to a common metadata schema (based on schema. org) and stored in a database (MAM/media asset manager, or MongoDB 3  ) to make it available for further processing. Analysis results, as well as the original metadata, are stored in CAS format to allow integration of different aligned layers of analysis. The process flow, pictured in Figure  3 , can be summarized as follows: 1. new data arrives (or gets imported) 2. preprocessing stage (a) make content available through unique URI (from central MAM) (b) create initial CAS with aligned metadata / text content and content URI (f) update queues i. mark as processed ii. add to queues for other processes that depend on previous analysis results 4. indexing when processing is complete for a content item (e.
Aligned data representation Much of the reasoning and cross-modal integration depends on an aligned view of the different annotation layers, e. g. , in order to connect person names detected from OCR with corresponding speakers from the speaker recognition component, or faces detected by the face recognition. The Apache UIMA 4  CAS (common analysis structure) representation is a good fit for the needs of the EUMSSI project as it has a number of interesting characteristics: • Annotations are stored "stand-off", meaning that the original content is not modified in any way by adding annotations. Rather, the annotations are entirely separate and reference the original content by offsets  • Annotations can be defined freely by defining a "type system" that specifies the types of annotations (such as Person, Keyword, Face, etc.
) and the corresponding attributes (e. g. dbpediaUrl, canonicalRepresentation,. ) • Source content can be included in the CAS (particularly for text content) or referenced as external content via URIs (e. g. for multimedia content) • While each CAS represents one "document" or "content item", it can have several Views that represent different aspects of that item, e. g. the video layer, audio layer, metadata layer, transcribed text layer, etc. , with separate source content (SofA or "subject of annotation") and separate sets of annotations • CASes can be passed efficiently in-memory between UIMA analysis engines • CASes can be serialized in a standardised OASIS format 5  for storage and interchange In the case of the EUMSSI project, the common base for alignment for different annotation layers referring to multimedia content is timestamps relative to the original content.
In this way it is possible to apply standard text analysis modules (that rely on character offsets) on the textual representation, while maintaining the possibility to later map the resulting annotations back onto the temporal scale. Timestamps will be represented in milliseconds in order to avoid floating point values. In this way, all annotations can be subtypes of the standard UIMA Annotation type 6 , which provides access to a number of utility functions that help find sets of overlapping annotations, retrieve annotations in offset order, etc. SofA-aware UIMA components are able to work on multiple views, whereas "normal" analysis engines only see one specific view that is presented to them. This means that e. g. standard text analysis engines don't need to be aware that they are being applied to an ASR view or an OCR view; they just see a regular text document.
There are some components of the EUMSSI platform, however, that do not integrate easily in this fashion. This is the case of computationally expensive processes that are optimized for batch execution. A UIMA AE needs to expose a process() method that operates on a single CAS (= document), and is therefore not compatible with batch processing. This is particularly true for processes that need to be run on a cluster, with significant startup overhead, such as many video and audio analysis tasks. It is therefore necessary to have an alternative flow mechanism for offline or batch processes, which needs to integrate with the processing performed within the UIMA environment. The main architectural and integration issues revolve around the data flow, rather than the computation. In fact, the computationally complex and expensive aspects are specific to the individual analysis components, and should not have an important impact on the design of the overall platform.
As such, the design of the flow management is presented in terms of transformations between data states, rather than from the procedural point of view. The resulting system should only rely on the robustness of those data states to ensure the reliability and robustness of the overall system, protecting against potential problems from server failures or other causes. At any point, the system should be able to resume its function purely from the state of the persisted data. To ensure reliability and performance of the data persistence, we expect to use a well-established and widely used database system such as MongoDB. Figure  4  shows the general flow of the EUMSSI system, focusing on the data states needed for the system to function. In order to avoid synchronization issues, the state of the data processing is stored together with the data, and the list of pending tasks can be extracted at any point through simple database queries.
MongoDB allows to stored structured information (corresponding to a JSON structure), so that the content of fields like ORIGINAL SOURCE METADATA can reflect whatever internal structure the original data had. The final applications are not expected to use the information stored in MongoDB directly, but rather access Solr indexes created from that information to respond specifically to the types of queries needed by the applications. Those indexes will typically be created from the CAS when all analysis steps have been performed. It is, however, possible to have indexing processes that only depend on a subset of analyses, and thus make content items (at least partially) accessible to the applications before they have been fully processed (which may take a relatively long time). The indexing processes can be managed in the same way as any analysis process, with their own queues that specify the necessary dependencies, and taking the current state of the CAS as input.
Conclusions and future work In this paper, we have presented the main goals and approaches of the EUMSSI project, which aims to innovatively integrate state-of-the-art text and A/V analysis technologies, semantic enrichment and reasoning, social intelligence and collaborative content-based recommendation, in order to build a multimodal, interoperable platform potentially useful for any application in need of automatic cross-media data analysis and interpretation, such as intelligent content management, personalized recommendation, real time event tracking, content filtering, etc. The project is still in an early stage, and many aspects will need to be defined later on. The different analysis modalities are handled by separate research groups that will each improve the individual types of analysis in their are of expertise.
Results and discussion The constitutive equations previously described are used to simulate a filled silicone behavior with the Matlab software. The parameters are identified from experimental data obtained during the uniaxial tensile test presented in Section 2. They are reported in Table  2. These parameters are then used to simulate the other experimental tests. The simulation of the first uniaxial tensile test with three increasing cycles is presented in Fig. 8(a). This figure shows that the silicone behavior is well predicted by the model for this experiment as the curves are nearly confounded. The prediction of the hysteresis loops size (calculated as the difference between load and unload) presented in Fig. 8(b ) is satisfactory for this test. Fig. 9(a)  shows the simulation of the planar tensile test with three increasing cycles.
Finite element implementation The model proposed in this paper is developed in the framework of isotropic and anisotropic invariants. To take into account the material incompressibility, a quasi-incompressible formulation of the strain energy function is needed: W ¼ W I 1 ; I 2 ; I ðiÞ 4 þ UðJÞ ð 7Þ where I 1 ¼ I À1=3 3 I 1 ; I 2 ¼ I À2=3 3 I 2 and I ðiÞ 4 ¼ I À1=3 3 I ðiÞ 4 are the incompressible invariants and J 2 ¼ I 3 ¼ detðCÞ the square of the volume variation. The model is implemented into the finite element software ABAQUS via a UMAT. Details about the numerical implementation of I 1 ; I 2 ; I 4 constitutive equations can be found in  [42] [43] [44] [45]. The finite element implementation needs the calculation of the second Piola-Kirchhoff stress tensor and the Lagrangian deviatoric tangent modulus.
5•x 2 , -∞ < x < ∞ (6) K 2 (x) = 0. 75 • 1 -x 2 , -1 < x < 1 = 0 (7) Smoothing parameters σ and h can be calculated using Gaussian distribution equations (Full Width at Half Maximum), but their empirical determination leads to similar results, since reasonable variations from their theoretical values do not change the final interpretation. Meteorological data used for NWR were obtained from continuous 1 min measurements carried out on the campus of the Polytechnic School (Ecole Polytechnique) (4 km east of the measurement site) with an A100R Campbell Scientific anemometer and a W200P Campbell Scientific weather vane at 25 m above ground level (a. g. l. ), above any impediment (e. g. , trees or buildings) that could affect the measurement of a particular wind sector.
Camera calibration is one of the fundamental photogrammetric tasks. The standard procedure is to apply an iterative adjustment to measurements of known control points. The iterative adjustment needs initial values of internal and external parameters. In this paper we investigate a procedure where only one parameter -the focal length is given a specific initial value. The procedure is validated using the freely available Damped Bundle Adjustment Toolbox on five calibration data sets using varying narrow-and wide-angle lenses. The results show that the Gauss-Newton-Armijo and Levenberg-Marquardt-Powell bundle adjustment methods implemented in the toolbox converge even if the initial values of the focal length are between 1/2 and 32 times the true focal length, even if the parameters are highly correlated.
INTRODUCTION Camera calibration is one of the fundamental photogrammetric tasks. The standard procedure is to apply an iterative adjustment to measurements of known control points. The iterative adjustment needs initial values of the internal and external orientation, and optionally any object points. The theory and formulation of radial and de-centring lens distortions from  (Brown, 1971)  have been adopted in close range photogrammetry as well as in e. g. the popular free Computer Vision Camera Calibration Toolbox for Matlab 1 and in the commercial Computer Vision System Toolbox for Matlab 2 based on the work by  Zhang (2000). The aid of coded targets is now the norm for off-line automatic camera calibration  (Fraser, 2013). Automatic camera calibration using self calibration is a standard in most of the image based dense matching tools available  (Snavely et al.
Data sets One calibration data set for each of five different camera-lens combinations was generated. The lenses were conventional, i. e. no fish-eye lenses were used. Details about the cameras are given in Table  1. Three data sets used the 2D Photomodeler calibration sheet consisting of an approximately 1-meter-square, 10-by-10 grid of black circular targets, including 4 coded targets used as control points. One data set used a 5-by-6-by-2 meter 3D calibration coded targets indoor test field. The final data set used a 60-by-35-by-20 meter array of control points measured on the INSA Building in Strasbourg  (Börlin and Grussenmeyer, 2013b). Images and the complete camera networks are shown in figures 1 and 2, respectively. Except in data set 5, the object and control points were measured by the automatic circular target measurement technique in Photomodeler Scanner 2012.
EXPERIMENTS For each data set, two experiments were performed to determine the pull-in range of the camera calibration with respect to the initial focal length estimate f0. For the FULL experiment, the full set of lens distortion (LD) parameters K1, K2, K3, P1, and P2 was estimated. The initial focal length f0 = mf * value was successively set to between m = 1/8 and m = 128 times the true value f *. Steps 1-5 of the algorithm in Section 2. 1 were used to determine the other initial values. In step 6 of the algorithm, the same initial values were given to each of the four bundle algorithms listed in Section 2. 2. The required number of iterations or failure to converge to the true values was recorded for each algorithm. A maximum number of 100 iterations was allowed for convergence. For the second experiment, a STABLE set of LD parameters was determined for each data set.
Unknown focal length and/or sensor size In this paper we used the actual sensor size and focal lengths, available either from the EXIF information or from knowledge about the used camera. Our data suggests that an initial value of f0 ≤ 32f * will give convergence, at least for geometrically strong networks with good image coverage. If the sensor size is unknown, we observe that the choice of unit for the sensor size and focal length is arbitrary, since only their ratios appears in the collinearity equations. Furthermore, said ratios are functions of the angle of view of the camera. Since it is reasonable to assume that the angle of view is restricted to a reasonable range, we suggest that the sensor height is fixed arbitrarily and the other internal parameters are scaled accordingly. Barring other knowledge, we suggest fixing the sensor height to 24 mm to match the 36-by-24 mm "35 mm" standard film format, in which case the calibrated focal length will be the "35 mm equivalent" focal length of the camera.
In this paper we investigated a camera calibration algorithm based on the freely available Damped Bundle Adjustment Toolbox for Matlab that required initial values of only one parameter -the focal length. The results show that the damped GNA and LMP bundle algorithms have a wide pull-in range, especially compared to the GM and LM algorithms, even on highly correlated camera parameter sets. This is consistent with the results reported by  Börlin and Grussenmeyer (2013a). The ability to converge with correlated parameters, enables a posteriori statistical analysis and exclusion of unstable parameters starting with the full parameter set rather than adding parameter incrementally. Other techniques for estimating the initial focal length, not investigated in this paper, include the DLT method for 3D targets by  Abdel-Aziz and Karara (1971)  and the relative orientation techniques by  Nistér (2004)  and  Snavely et al.
VirHostNet release 2. 0 (http://virhostnet. prabi. fr) is a knowledgebase dedicated to the network-based exploration of virus-host protein-protein interactions. Since the previous VirhostNet release (2009), a second run of manual curation was performed to annotate the new torrent of high-throughput proteinprotein interactions data from the literature. This resource is shared publicly, in PSI-MI TAB 2. 5 format, using a PSICQUIC web service. The new interface of VirHostNet 2. 0 is based on Cytoscape web library and provides a user-friendly access to the most complete and accurate resource of virus-virus and virus-host protein-protein interactions as well as their projection onto their corresponding host cell protein interaction networks. We hope that the VirHostNet 2. 0 system will facilitate systems biology and gene-centered analysis of infectious diseases and will help to identify new molecular targets for antiviral drugs design.
Nowadays, thousand viral genomes are fully sequenced and more than 2. 5 million proteins are annotated in databases. However, their molecular and biological functions remain largely unknown or underdetermined. Since the beginning of the molecular biology revolution, numerous small-scale experiments were designed to better characterize the function mediated at the virus/host (VH) protein-protein interaction (ppi) interface. This trend has been emphasized in the last 10 years by the deluge of yeast two-hybrid (Y2H) and tandem affinity purification (TAP) data produced to characterize VH ppi in a systems biology perspective  (1) (2) (3) (4) (5) (6). The VirHostNet knowledgebase release 1. 0 (7) was launched in this context in 2007. The VirHostNet manual annotations were used during these last years as a gold standard in many VH interactome experiments, but also as a staring point for systems biology studies  (2) (3) (8) (9) (10) (11) (12) (13).