dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 3 A script written for the database allows further exploration. In Figure  5  (bottom) the interior of each component node is color coded according to the Regional division of the World Bank that prepared the project. The outward link is coded with the color of the target component's region. (See the Annex. ) This allows quick visual inspection of the extent to which project concepts diffuse across geographic and organizational boundaries. The exterior ring of each node is color-coded with the project's IEG outcome rating. (This is more visible when the view is zoomed. ) These color assignments are customizable, so that links can be colored according to target or source, and by region or rating.
3. 4 Validation of the network presents a challenge. Preliminary inspection of the derived network graph showed examples of causally linked components. For instance, the appraisal document for the Sri Lanka Renewable Energy for Rural Economic Development, component p077761_0 has the following text in the introduction (but not in the component description text contained in the database) "The Project design aims to build a sustainable market for renewable energy through IDA and GEF support. Of the six distinct elements in the Project, the four that support grid-connected and off-grid generation of energy from renewable sources all have antecedents in the ESD project launched in 1997. Accounting for nearly 90 percent of total Project costs, these four components build on the success of the ESD project laid in small hydro, wind and solar power and in promoting energy efficiency.
Results First, we tested TRAM on a range of image datasets, both simulated and real (Supplementary Figs  S1-S2 ). Here we focus on four-exemplar fluorescence datasets: fluorescent beads, quantum dots, quantum dots on a DNA origami scaffold and stained pulmonary endothelial cell. First, 40 nm-diameter fluorescent bead (i. e. , sub-resolution) image data were acquired using a CLSM (pixel size of 20 nm), whilst translating the sample 30 times in the imaging plane in the order of 100 nm-steps in an XY-grid pattern. We applied our TRAM restoration method to these images. We acquired images from the same field of view using gSTED to allow direct assessment of results from CLSM, gSTED and TRAM (Fig. 1b : top row). As seen from the full view of the these images, there are more than 100 bright spots observed in the confocal image.
The results are pleasing given the differences in sample preparation and imaging modality between AFM and TRAM. The TRAM restoration delivered, from data acquired using a standard confocal microscope, a very substantial improvement in spatial resolution and contrast, revealing sub-resolution rectangular structures with the shape we designed. Having demonstrated that TRAM could deliver super-resolution images in a straightforward manner from relatively sparse samples, not requiring special instrumentation, we finally set out to acquire data from a biological sample. A principal requirement of biological imaging is multi-color labeling; however it remains challenging to acquire multi-color super-resolution image data. STED 2 for example, commonly utilizes a single depletion laser, meaning that special combinations of organic dyes are required.
Discussion It is important to note that as with all other techniques, TRAM has limitations in its performance. It relies on intensity information at pixel level, so when bleaching occurs in a local region of an image, restoration is not possible there, but it does not affect other regions in the image. When there are changes in image structures between the translation images, either due to motion induced blur or dynamics, TRAM has difficulty in its present form and so is best suited to fixed samples (in common with many of the current super-resolution modalities). TRAM restoration currently requires a few hours of computation for a 256 × 256 image by using a desktop computer running on MATLAB, however, its performance will improve with algorithmic optimization, processing parallelization and with computing hardware advances.
We note that if optical sectioning techniques can acquires 3D diffraction-limited images with the depth (axial) information, TRAM can in principle be extended to be a 3D restoration method from the current 2D technique. Simulation. We applied our TRAM restoration method to a number of data sets and validated the results by comparing to the ground truths. We first tested our method on a 2-D standard resolution chart and a synthetic biological image containing blobs and ridges of varying sizes and orientations, from which we explore some usual scaling laws between the quality of restoration and number of LR images in different noise levels. Finally, we performed TRAM on two optical microscopy data sets, beads and quantum dots, with known ground truth. In all the tests the SR restoration process was measured by the mean squared difference-norm (MSDN) of the restored images between two adjacent iterations.
Software. We have implemented the TRAM algorithm in Matlab (Mathworks). The software has been attached here for demonstration purpose, which includes the TRAM program and a set of translation LR beads images. Users can run the program to show the evolution of how a TRAM SR image is restored from these LR images. The results can be compared with gSTED image from the same view area. The software can run in Windows7 64 bit and Linux Ubuntu 64 bit machines.
Sample preparation. QDot 625 (Invitrogen) was diluted 1:1,000,000 in phosphate buffered saline (PBS). Coverslips were coated with CellTak (BD Biosciences) according to the manufacturer's instructions. Diluted quantum dots were incubated on the coated coverslips for 1 hour prior to imaging in PBS. Fixed cell datasets were acquired using the FluoCells pre-prepared slide #2 (Invitrogen) which contains bovine pulmonary artery endothelial cells (BPAEC) stained with Texas Red-X phalloidin, anti-bovine α -tubulin and BODIPY FL labelled secondary antibody, and DAPI. Quantum dot -DNA origami. All short staple strands were purchased from Invitrogen (China) and used as received. M13mp18 single stranded DNA was purchased from New England Biolabs. Chemicals were purchased from Sigma-Aldrich. Quantum dots were purchased from Invitrogen (China).
Then the surface was rinsed using deionized water and dried by air. All AFM images were obtained using a Multimode Nanoscope VIII instrument (Bruker) under tapping mode in air with NSC11 tips (μ Masch). Fluorescent image data acquisition. Quantum dot calibration data were acquired on an inverted IX81 microscope (Olympus) using a 150 × 1. 45 NA objective. Illumination was provided by a fully motorized four laser TIRF combiner coupled to a 405 nm 100 mW laser under widefield illumination. The sample was laterally translated using a motorized stage (ASI). Image data was collected using an Orca-Flash 4. 0 s CMOS camera (Hamamatsu) which in combination with a 1. 6 × magnifier in the image path provided an effective pixel size of 27 × 27 nm. Ten frames were acquired at each position before translation of the stage to the next position.
Fixed cell and DNA origami image data were acquired on an SP5 gSTED SMD laser scanning confocal microscope (Leica) using a 60 × 1. 4 NA objective. Images 4096 × 4096 were acquired with a pixel size of 6 nm (for Fig. 4 ) or 19 nm (for Fig. 3 ). A single frame in each channel was acquired before translation of the stage to the next position.
Abstract. The increasing availability of text information coded in many different languages poses new challenges to modern information retrieval and mining systems in order to discover and exchange knowledge at a larger world-wide scale. The 1st International Workshop on Modeling, Learning and Mining for Cross/Multilinguality (dubbed MultiLingMine 2016) provides a venue to discuss research advances in cross-/multilingual related topics, focusing on new multidisciplinary research questions that have not been deeply investigated so far (e. g. , in CLEF and related events relevant to CLIR). This includes theoretical and experimental on-going works about novel representation models, learning algorithms, and knowledge-based methodologies for emerging trends and applications, such as, e. g. , cross-view cross-/multilingual information retrieval and document mining, (knowledge-based) translation-independent cross-/multilingual corpora, applications in social network contexts, and more.
Objectives, topics, and outcomes The aim of the 1st International Workshop on Modeling, Learning and Mining for Cross/Multilinguality (dubbed MultiLingMine 2016 ),  6  held in conjunction with the 2016 ECIR Conference, is to establish a venue to discuss research advances in cross-/multilingual related topics. MultiLingMine 2016 has been structured as a full-day workshop. Its program schedule includes invited talks as well as a panel discussion among the participants. It is mainly geared towards students, researchers and practitioners actively working on topics related to information retrieval, classification, clustering, indexing and modeling of multilingual corpora collections. A major objective of this workshop is to focus on research questions that have not been deeply investigated so far.
In this paper we present MOoD-TC (Multilingual Ontology Driven Text Classifier  [3, 13] ), a highly modular system which has been conceived, designed and implemented to be customized by the system developer for obtaining different domain-dependent behaviors, always centered around the multilingual text classification process. The original contribution of this paper is the exploitation of the core "multilingual word identification" functionalities of MOoD-TC for a challenging scenario in the e-Health domain, where classification is a by-product of disease symptoms identification in multilingual pieces of text, driven by a standard symptoms ontology. A customization of MOoD-TC with an ad-hoc module equipped with pre-and post-processing facilities suitable for the scenarios that motivate our work, is also described.
Luckily, many domain ontologies exist, in particular in the biomedical domain. Panacea  [7] , the Ontology for General Medical Science 3 , and the Gene Ontology 4 are just a few recent examples, besides the "symptoms ontology" used for our experiments and discussed in Section 5. 1 http://www. ibm. com/smarterplanet/us/en/ibmwatson/watson-oncology. html 2 A clinical report is written by a doctor. 3 https://bioportal. bioontology. org/ontologies/OGMS 4 http://geneontology. org/ 4 MOoD-TC MOoD-TC has been developed as part of Silvio Beux' Masters Thesis  [3] , starting from  [13]. Its aim is to classify multilingual textual documents according to classes described in a domain ontology. MOoD-TC consists of the Text Classifier (TC) and the Application Domain Module (ADM). It provides a set of core modules offering functionalities which are common to any text classification problem (text pre-processing, tagging, classification) plus a customizable structure for those modules which can be implemented by the developer in order to offer application-specific functionalities.
It returns a classification of the text w. r. t. the ontology taken as input. The classification performed by TC which is implemented in Java and exploits the Language Detector Library 5  , BabelNet  [16] , and TreeTagger 6. The Language Detector Library detects, with a precision greater than 99%, 53 languages making use of Naive Bayesian filters. It is devoted to recognize the language L o of the ontology o and the language L d of the textual document d. The TreeTagger tool performs the tagging of d in order to obtain, for each word w ∈ d different from a stop word, its lemma (the canonical form of the word) and its part of speech (POS). This information is used by BabelNet to perform the translation of w into the ontology language. Finally, the translated word w is searched inside the ontology and contributes to the classification of d in the category modeled by the ontology concept c having the same semantics as w.
We stress that our experiments in exploiting MOoD-TC for symptom identification did not require to build any new ontology. Rather, consistently with the good principle of reusing existing software whenever available and, in particular, reusing existing ontologies, we just passed the symptoms ontology as input to the TC, obtaining the results discussed in the next section. In the sequel we discuss our initial experiments with phrases in five different languages (English, French, German, Italian, Spanish), where symptoms are identified by the TC module. The classification of two sample sentences is shown below, where the TC GUI screenshot associated with each sentence shows the ontology concepts which appear in the text along with the number of their occurrences in the text. The experiments have been carried out on 32 sentences for each of the 5 languages, for a total of 160 sentences.
On the x axis the diseases are reported (labels are omitted) and on the y axis the number of false negatives for disease is reported: each line in the graphic is associated with one language. language), the identification fails. This problem is due to the way the root of a word is computed, and to the way words are managed in BabelNet. What emerges from Figure  4  is that false negatives have a very similar behavior despite the language of the sentence. This is again due to the two reasons discussed above. Despite these problems, which have a clearly understood motivation and which can be addressed by extending the ontology and by refining the management of word root extraction, MOoD-TC has demonstrated to be a flexible and ready-to-use approach for multilingual symptoms identification driven by a standard ontology we retrieved on the web.
Conclusions and Future Work In this paper we presented the MOoD-TC architecture showing its possible use in the symptoms identification problem. The speech-to-text pre-processing stage can be faced using existing tools, and the post-processing stage with a translation of the identified symptoms into the doctor's language can be addressed using Ba-belNet, in the same way we exploit BabelNet for bridging the text, whatever its language, and the ontology. The more challenging post-processing stage of supporting the user in providing a diagnosis given a set of identified symptoms could be addressed by means of sophisticated expert system such as the old and well known MYCIN  [4]  and more recent projects (http://www. easydiagnosis. com/, https://www. diagnose-me. com/, [2] ), some of which are ontology-driven  [1].
Datasets In order to adjust the parameters of the proposed feature extraction method, a multilingual corpus of literary works was compiled. Due to the cross-lingual nature of the experiments, documents in different languages created by the same author are required. Literary translation is believed to keep the markers from the original author and the influence of the translator is weak  [16] , therefore the corpus used in the experiments is formed by original works by 8 authors and translated novels from the same 8 authors. It includes two datasets: Dataset 1, a balanced dataset of original documents and Dataset 2 a unbalanced extended version including translations. Dataset 1 contains 120 literary texts from 8 different authors (15 documents per author) in 4 different languages (English, Spanish, German and French) as can be seen in Table  2.
Estimating optimum parameter values The first parameter to be set is n the number of samples for each sample size i that is necessary to obtain a representative figure for average and dispersion values. An empirical study has been performed with 10 to 2000 samples of each size, using a Random Forest classifier and leave one out cross validation. The results of the classification using Fragments and bags-of-words for Dataset 1 are shown on Figure  3. The number of correctly classified documents increases as the number of samples increases until a stable value is reached. Fragments and bags-of-words behave differently with more variation in the bags-of-words samples. Two threshold levels can be identified in figure  3 , the first threshold is around the value of 200 samples, and the second threshold is around 700 samples where the results are more stable.
The results, grouped per number of different sample sizes (M ) and hence per total number of features, are shown in Figure  4. Figure  4  shows the results for fragments, bags-of-words and the combination of both for Datasets 1 and 2. The results from the different combinations of sample sizes show different responses to Dataset 1 and Dataset 2. The different nature of these two datasets explain the different behaviour of the type of samples for each dataset. Fragments are more powerful at discriminating between originals in a balanced setting whereas bags-of-words perform poorly when each author is represented by documents in only one language. On the other hand, bags-of-words provide stronger results for the more difficult problem presented in Dataset 2 where translations are included in the dataset.
In both scenarios, the combination of both types of samples, BFS, provides the best results. In terms of the final size of the feature set, which combines the type of sample and the number of sample sizes i, there is no significant improvement after 2 sizes are combined. The final size of the feature set is therefore N = 2(8 F + 8 B ) = 32. A closer look at the combination of sizes that produce the best results show sizes 500 and 1000 obtaining the highest accuracy. Preliminary evaluation of BFS applied to CLAA using the same cross-validation method (leave one novel out) and the same dataset as Bogdanova and Lazari-dou  [3]  shows that BFS achieves better classification results (0. 47) than high level features without the use of machine translation (0. 31). In this particular experiment, 27 documents plus 7 which are translations of one of the 27 are used, with the final dataset being formed by 275 texts extracted from the 34 original documents.
For this reason, leave one novel out is used to avoid the classifier being trained on texts from the same document (or translations of it). Every time leave one novel out is performed on this dataset, a large number of texts are removed from the training data, hence the training set is small, which added to the short length of the texts, affects the overall classification performance. Machine translation methods achieve better results but are limited by the availability of resources in the given languages as well as the requirement to identify the target language beforehand.
Profile-based Translation in Multilingual Expertise Retrieval Hossein Nasr Esfahani, Javid Dadashkarimi, and Azadeh Shakery {h_nasr,dadashkarimi,shakery}@ut. ac. ir School of ECE, College of Engineering, University of Tehran, Iran Abstract. In the current multilingual environment of the web, authors contribute through a variety of languages. Therefor retrieving a number of specialists, who have publications in different languages, in response to a user-specified query is a challenging task. In this paper we try to answer the following questions: (1) How does eliminating the documents of the authors written in languages other than the query language affect the performance of a multilingual expertise retrieval (MLER) system? (2) Are the profiles of the multilingual experts helpful to improve the quality of the document translation task? (3) What constitutes a good profile and how should it be used to improve the quality of translation? In this paper we show that authors' documents are usually related topically in different languages.
Multilingual information retrieval (MLIR) is a well-known research problem and has been extensively studied in the literature  [10]. There are two options for scoring documents written in languages other than the language of the query; translating the query into all the languages of the documents, or representing all the documents in the language of the query. In MLIR it has been shown that the second approach outperforms the first one in the language modeling framework  [9]. In the current paper we are going to cast such an approach to multilingual expert retrieval (MLER). Indeed, our new problem is to retrieve experts who are contributing in multiple languages. In this research we choose the document translation approach for our problem. It is noteworthy that no translated document in the traditional sense is produced, but rather a multilingual representation of the underlying original document that is suitable for retrieval, but not for consumption by a reader, is constructed.
Furthermore, proper weighting of translations has always had a major effect on MLIR performance. Therefore improving the translation model based on user profile can supposedly lead to better MLER performance. We are trying to answer the following research questions in this paper: 1. How does eliminating the documents of the authors written in languages other than the query language affect the performance of an MLER system? 2. Are the profiles of the multilingual experts helpful to improve the quality of the document translation task? 3. What constitutes a good profile and how should it be used to improve the quality of translation? Our findings in this paper reveal that multilingual profiles of the experts are useful resources for extraction of expert-centric translation models. To this aim we propose two profile-based translation models using (1) maximum likelihood estimation (PBML), and (2) topicality of the terms (PBT).
Experimental Setups We used the bilingual TU expert collection  [5]  in our experiments. This collection contains a number of documents written by scientists, researchers, and support staff from Tilburg University The collection is provided in an English-Dutch environment. Table  1  shows some statistics one the dataset and Figure  2  shows the contribution of each expert on the set. As shown in Figure  2 , experts have enough documents in both languages which makes the dataset suitable for our tests. Parameter Settings: In all experiments, the Jelinek-Mercer smoothing parameter λ is set to the typical value of 0. 9. All free parameters, particularly the constant controlling values of the linear interpolations, are set using 2-fold cross validation over the collection. The noise constant in the EM algorithm is set to 0.
The reason for marginal improvements in this dataset goes back to the high performance of the monolingual results. As shown in the table the results of the mono-lingual runs are competitive to the MLER ones (90. 45% and 93. 64% of PBT in EN-EN and NL-NL runs respectively). We did further experiments to directly study the effect of the proposed profile-based document translation method. We opted CLER instead of MLER for this purpose. In Table  3  experimental results of a number of CLER runs are provided. These experiments are done only on the documents which are in outof-the-context languages. To shed light on the effectiveness of the profile-based translation model, we experiment on a subset of the queries which are ambiguous. A query is considered to be ambiguous if at least one of its terms is ambiguous.
English (EN) Dutch (NL) TOP-1 PAR PBML PBT TOP-1 PAR PBML PBT MAP 0. 1945 0. 1955 0. 1949 0. 2026 012 MAP 0. 1221 0. 1341 0. 1455 0. 1458 01 P@5 0. 1195 0. 1208 0. 1229 0. 1275 P@5 0. 0712 0. 0829 0. 0883 0. 093325 P@10 0. 087 0. 0867 0. 0885 0. 09015 P@10 0. 0585 0. 0647 0. 0669 0. 0691 RI --0. 1566 -0. 0482 0. 0172 RI -0. 0196 0. 1538 0. 2215 Table  3 : Experimental results for different translation methods for cross-lingual expert retrieval over ambiguous queries. To sum up our findings we answer the following research questions: 1. How does eliminating the documents of the authors written in languages other than the query language affect the performance of an MLER system? Regarding the competitive mono-lingual results in Table  2  in the TU dataset we can claim that the authors repeat majority of their contributions through languages and so their publications in only one language are almost good but not complete indicators of their expertise.