dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition 1 (Differential Privacy [5]). A randomized mechanism M satisfies -differential privacy with > 0 if for any possible set of workers' profiles P and P such that P is P with one additional profile (or one profile less), and any possible set of output O ⊆ Range(M), Pr[M(P) ∈ O] ≤ e × Pr[M(P ) ∈ O]. ( 1 ) Theorem 1 (Compositions  [14] ). Let M be a randomized mechanism which satisfies -differential privacy. Then:  (1)  executing M i times sequentially over the dataset D, each time with a privacy parameter set to i , provides ( i i )differential privacy (sequential composition), and (  2 ) executing M over i disjoint subsets of the dataset D, each time with a privacy parameter set to i , provides (max i i )-differential privacy (parallel composition). Theorem 2 (Post-Processing  [7] ).
Experimental Settings We evaluated the performance and quality reached by the MWF, CWF, and TWF weight functions. We implemented several comparison baselines: the Hamming We use a synthetic taxonomy (Perfect34) that we generated from a perfect balanced tree of height 3 and branching factor 4, thus containing 85 nodes of which 64 are leaves. Our experiments were performed over three synthetic datasets. (1)  In the first dataset (Normal), the skills (workers and tasks) are randomly sampled from the leaves of the taxonomy following a normal distribution N ( √ r cos θ, √ r|sin θ|) with θ randomly chosen in [0, 2π) for profile, and r = 0. 06, corresponding to around 30% of skills per profile. The resulting distribution is given on Figure  4. (2) In the two other datasets, the skills of each worker's and task's profile are sampled uniformly at random in the leaves of the taxonomy at varying rates.
We call Bernoulli01 the dataset drawn from B(1, p = 0. 01), and Bernoulli1 the one from B(1, p = 0. 1). All the assignments were made between 100 workers and 100 tasks. The experiments presented in this paper were carried out using the Grid'5000 testbed  15. All experiment were conducted on machines running Debian Jessie (64-bits) with an Intel(R) Xeon(R) E5-2680 v4 at 2. 4 GHz and 16GB of RAM. The code was written in Python 16  3. 5. 3 using NumPy 17  1. 12. 0, SciPy.
Performance Our approach does not generate any network overhead since a perturbed skills vectors has the same length as an original skills vector 21. We consequently focus on the wall-clock time taken to compute the weight functions. Table  1  shows the average time among one thousand calls to each weight function, as well as their asymptotic complexities. The taxonomy is Perfect34 and the dataset Bernoulli1. We assume pre-computed lowest-common-ancestors and shortestpaths, and that the distance used by CWF is linear in time. We observe that these times are insignificant with respect to typical computation times of the assignment algorithm.
Quality Figure  5  shows the evolution of the two quality measures q rel and fpa, according to the bit flipping probability Pr f lip , of the complete set of weight functions and on the random matching, on the synthetic taxonomy Perfect34. First, let consider the Bernoulli datasets. We observe globally that the weight functions are rather robust against the differentially-private perturbation. TWF exhibits a noticeable q rel measure equal to 0 on the two Bernoulli datasets. This is actually due to the 0 cost of the unperturbed assignment which makes q rel equal to 0 whatever the cost of the perturbed assignment. The later exhibited a value similar to the cost of TWF on the Normal dataset. Given its high relative quality, Hamming happens to be a robust metric. However, its fpa score is 0 (or close to) on the two Bernoulli datasets.
This is not the case for the other metrics on Bernoulli01, especially for MWF and AWF and except TWF. Second, on the normal dataset the weight functions using the taxonomy (i. e. , TWF, CWF, Fig. 5 : q rel and fpa for each weight function, with respect to Pr f lip , for the Perfect34 taxonomy. fpa of non-perturbated assignments is dashed AWF) have a relative quality close to 1 robust and achieve a reasonnable, similar, fpa score.
Conclusion We have described in this paper a lightweight privacy-preserving approach to the problem of assigning tasks to workers. Our approach allows each worker to perturb her skill profile locally in order to satisfy the stringent differential privacy model without any need for additional communication or computation cost. We have proposed novel weight functions that can be easily plugged in traditional centralized assignment algorithms, and that are able to cope with the differentially private perturbation by leveraging the presence of a skill taxonomy. Experiments performed over a synthetic taxonomy and synthetic datasets show that our weight functions allow perturbed assignements to reach quality levels close to non-perturbed assignments. Future works include collecting a large-scale real-life skill dataset, and continue exploring the performance vs quality tradeoff by designing alternative profile perturbation strategies (e.
Phylogenetic analysis of TRs Vertebrate TR, RAR and Rev-erb amino acid sequences were retrieved from Genbank (see Table  S1  in Supplementary Material available online at http://www. agialpress. com/journals/ nurr/2017/101287/) and aligned using MUSCLE 3. 8  [31]. For phylogenetic reconstructions, we restricted the analysis to the C-term part of the DBD and the LBD since we were able to retrieve and identify only these domains for all genes and species studied (see Table  S2  in Supplementary Material available online at http://www. agialpress. com/journals/nurr/ 2017/101287/). Trees were generated using the Maximum Likelihood (ML) method under a JTT substitution matrix plus an eight category gamma rate correction ( estimated) with the proportion of invariant sites estimated. 1000 bootstrap replicates were performed to evaluate the branch support.
Four TR orthologs are present in lamprey The genome of the sea lamprey Petromyzon marinus has been sequenced and assembled, and these data are available in Ensembl  [10]. By running a TBLASTN search on the genome using the lamprey TR1 (Genbank DQ320317. 1) and TR2 (Genbank DQ320318. 1) as queries, we retrieved three loci containing TR-related coding sequences. One of them encodes the previously reported TR2 and the two others contain novel sequences: ENSPMAG00000000524 doi:10. 11131/2017/101287 (referred to as Pm524) on scaffold GL482050, and ENSPMAG00000008742 (referred to as Pm8742) on scaffold GL477881. Interestingly, when we look for the already known TR1 and TR2 in the published genome of P. marinus, we are able to retrieve TR2 under the locus ENSPMAG00000005715 on scaffold GL478356, but not TR1.
When we Blast TR1, the best hits match on the scaffold GL478356 with 78. 3% of identity. Nevertheless, the TR on GL478356 is more similar to TR2 with 96. 5% of identity. Thus, we anticipate that TR1 belongs to an unsequenced region of the lamprey genome. Using the gene prediction software Augustus  [30] , we retrieved the protein sequence of Pm524 and Pm8742. For Pm524, we were able to retrieve the C-terminal region of the DBD and the complete LBD, which represent 293 amino acids. For Pm8742, we were able to retrieve the complete DBD and the complete LBD, which represent 362 amino acids, but not the A/B region. The alignment of Pm524 and Pm8742 with lamprey TR1 and TR2 and human TR and TR reveals a high level of sequence identity on aligned regions (Figure  1 ). Pm524 has 72. 1% of amino acid identity with lamprey TR1, 67.
Data source and methods This retrospective multicenter study was approved by the ALWP of the EBMT and included adults (≥18 years) diagnosed with de novo MPAL and receiving HSCT from a matched related or unrelated donor. Patients with MPAL transplanted between 2000 and 2014 were included in the study. Demographics, MPAL disease characteristics, transplantation and post-transplantation data were extracted from the EBMT database (Med-A forms). The list of centers contributing patients to this study is available in the Online Supplementary Appendix. The EBMT is a voluntary working group of more than 600 transplant centers that are required to report all consecutive HSCT procedures and their follow up once a year. Audits are routinely performed to determine the accuracy of the data. Patients have been required to provide informed consent authorizing the use of their personal information for research purposes since 1990.
The purpose of the propensity score-matching strategy was to reduce confounding effects of these variables, and strengthen causal inferences. 18 Details about the patients used for matching are given in the Online Supplementary Appendix. All tests were two-sided and P<0. 05 was considered statistically significant. Analyses were performed using the R statistical software v. 3. 2. 3 (available online at: http://www. R-project. org), and propensity score analysis was performed using the 'MatchIt' program (last accessed May 18 th , 2015; http://cran. project. org/web/packages/MatchIt/MatchIt. pdf). Patients with missing values were excluded from propensity score analyses.
Discussion Mixed phenotype acute leukemias are high-risk acute leukemias often with poor cytogenetics. As in other types of acute leukemia, the prognosis can potentially be improved by allo-SCT. We present here, extracted from the EBMT database, the largest study of patients with MPAL who underwent allo-SCT. Our results (56. 3% OS at 3 years and 46. 5% LFS at 3 years, with an RI of 31%) are highly encouraging and definitely improved over registry data from the Surveillance, Epidemiology and End Results Program (SEER). In the SEER study, a survival of 20-40% at three years was reported for patients aged over 20 years. 6 In Table  4 , a synopsis with outcomes of previous smaller studies investigating allo-SCT for MPAL is presented. In single institution studies, 9-59 patients with MPAL were transplanted.
Procedures for immunofluorescence labeling were performed as described previously  (Ogawa et al. , 2006). Images of immunofluorescence were captured using an Axio-imager Z1 microscope (Carl Zeiss) or Axio-observer Z1 microscope (Carl Zeiss) fitted with an AxioCam digital camera (Carl Zeiss). Images were taken using 20ϫ/0. 8 numerical aperture (NA), 40ϫ/1. 0 NA, 40ϫ/0. 75 NA, or 63ϫ/ 1. 4 NA objectives. Images were then collected by Zen (Carl Zeiss) acquisition software. Measurement of fluorescence intensity and Sholl analysis was performed using FIJI and Zen software. In some cases, Z-stacks and 3D reconstructions were performed using Zen software. Superresolution Stochastic Optical Reconstruction Microscopy (STORM) and DNA-PAINT. Twelve days in vitro (DIV) neurons were fixed using 4% PFA for 10 min.
This paper presents the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. It recalls the design of the datasets, presents their final construction and statistics and the methods used to evaluate system results. 13 runs were submitted to the shared task by 4 teams, covering three of the four proposed language pairs: French-English (7 runs), German-English (3 runs), and Chinese-English (3 runs). The best F-scores as measured against the gold standard were 0. 84 (German-English), 0. 80 (French-English), and 0. 43 (Chinese-English). Because of the design of the dataset, in which not all gold parallel sentence pairs are known, these are only minimum values. We examined manually a small sample of the false negative sentence pairs for the most precise French-English runs and estimated the number of parallel sentence pairs not yet in the provided gold standard.
Introduction Shared tasks and the associated datasets have proved their worth as a driving force in a number of subfields of Natural Language Processing. However, very few shared tasks were organized on the topic of comparable corpora. Therefore, we endeavored to design and organize shared tasks as companions of the BUCC workshop se-ries on Building and Using Comparable Corpora. The First BUCC Shared Task  (Sharoff et al. , 2015)  tackled the detection of comparable documents across languages. The Second BUCC Shared Task, 1  presented here, addresses the detection of parallel sentences across languages in nonaligned, monolingual corpora. Let us recall the overall goals, design and principles of this task, which were introduced in  (Zweigenbaum et al. , 2016). A bottleneck in statistical machine translation is the scarceness of parallel resources for many language pairs and domains.
In this purpose we aimed to select as insertion points sentences that were similar in topic to the inserted sentences. We implemented this by indexing with the Solr search engine each English sentence of the monolingual corpus (English Wikipedia dump, converted to text and split into sentences) and each French sentence of the monolingual corpus (French Wikipedia dump, converted to text and split into sentences). For each sentence pair in the parallel corpus (French-English News Commentary), we queried Solr to find the most similar French sentence and English sentence for this pair; if hits were found for both languages, we recorded as insertion point for the French parallel sentence the French sentence found, and as insertion point for the English parallel sentence the English sentence found.
py, 4  to include sentence splitting (with NLTK). Since template processing was the cause of a large number of idiosyncrasies in the converted Wikipedia text, we removed the sentences that contained a template. 3. The original monolingual texts should contain as few 'natural' parallel sentence pairs as possible. Since interlinked Wikipedia articles are a common source of parallel sentence pairs, we ensured that a given dataset never contained sentences from such a pair of documents. 4. When the two sentences in a parallel pair are inserted in the monolingual corpora, there is no particular reason for them to be positioned in similar locations in the two corpora. Therefore, once a corpus has been generated this way, splitting it into training and test would be likely to separate a number of parallel pairs.
Name Affiliation Language pairs VIC Vicomtech-IK4, Donostia / San Sebastian, Gipuzkoa, Spain de-en (3), fr-en (3) RALI RALI -DIRO, Université de Montréal, Montréal, Québec, Canada fr-en (3) JUNLP Department of Computer Science and Engineering, Jadavpur University, India fr-en (1) zNLP LIMSI, CNRS, Université Paris-Saclay, Orsay, France zh-en (3) Table  2 : Shared task systems also ensures that no interlinked pair of pages is distributed to the same split. Indexing, searching and sentence insertion were then performed on each split separately. Since the training and test sets for a given language pair were generated with the same process and parameters, they received very similar numbers of parallel sentence pairs. This process was applied to five languages (Chinese (zh), English (en), French (fr), German (de), Russian (ru)) to produce four bilingual datasets, each split into sample, training, and test data.
Participants and systems About 17 teams downloaded datasets, among which four teams submitted runs: VIC (Spain)  (Azpeitia et al. , 2017) , RALI (Canada)  (Grégoire and Langlais, 2017) , JUNLP (India)  (Mahata et al. , 2017) , and LIMSI (France: 'zNLP')  (Zhang and Zweigenbaum, 2017). Table  2  gives more detail about teams and runs. All systems had to include a way to cope with the bilingual dimension of the task. This was addressed with pre-existing dictionaries (LIMSI), machine translation systems (JUNLP, LIMSI), word alignments obtained from parallel corpora (VIC), or bilingual word embeddings trained from parallel corpora (RALI). Cross-language sentence similarity was then handled by Cosine similarity (JUNLP, LIMSI, RALI) or the Jaccard coefficient (VIC), possibly with weighting (a function of frequency: VIC; tf.
Automatic evaluation We present here the evaluation results for the submitted runs for each language in turn. As explained above, these results are based on the artificially inserted translation pairs. In each table we show the precision, recall and F1-score of each run in percentages. Because this synthetic dataset represents an approximation of a real task, there is no point in computing precise scores: we round the computed percentages to the nearest integer. Additionally, we observed that some participants took into account the prior probability of translation pairs in the training datasets. Given that the test dataset was announced to be generated in the same way as the training dataset, they targeted a number of translation pairs in the test that was consistent with this prior probability.
We therefore display this number of translation pairs in the tables too. Three teams submitted runs on the French-English (fr-en) language pair. In addition to these runs, Table  3  presents the minimum, maximum, median, mean and standard deviation for each measure. The initial JUNLP submission had a bug which was fixed a couple of days later; we show the results of the fixed submission in italics, but did not include it in the additional statistics. The VIC results confirm the strategy described in run name sys n P (%) R (  %  F1-scores obtained by VIC for German-English are higher than those they obtained for French-English, with similar recalls. The only difference in the two corpora in terms of statistics is that the German-English dataset was more balanced in its numbers of monolingual sentences, but other differences linked to the intrinsic properties of German and French or to the resources used to train the system for these two languages are likely to have an effect too.
One team submitted runs on the Chinese-English (zh-en) language pair, therefore we do not report min, max and other statistics. The results are displayed in Table  5  Zweigenbaum, 2017), zNLP3 was optimized for precision: this is confirmed by its results on the test set. Overall, the results are lower than the best runs on the fr-en and de-en datasets. Various hypotheses can be proposed to account for this difference, including the different types and sizes of the resources used for translation in VIC and zNLP, the specific methods used in the two systems, and differences in intrinsic language properties.
Complementary human evaluation Were we to know which 'natural' translation pairs existed in the test datasets beyond the translation pairs we inserted, would the results be very different? We did not have resources to perform an extensive human evaluation to answer this question, therefore we designed a minimal experiment on the French-English language pair. In the VIC and RALI runs, we selected the run with the best precision and randomly drew 20-pair samples. A French native speaker with good command of English examined each sample and scored it according to the grades used in the SemEval 2016 cross-language sentence similarity task  (Agirre et al. , 2016 ): (5) The two sentences are completely equivalent, as they mean the same thing; (4) The two sentences are mostly equivalent, but some unimportant details differ; (3) The two sentences are roughly equivalent, but some important information differs or is missing; (2) The two sentences are not equivalent, but share some details; (1) The two sentences are not equivalent, but are on the same topic; (0) The two sentences are on different topics.
Conclusion We presented the design and results of the second BUCC 2017 Shared Task, which consisted in spotting parallel sentences in comparable corpora. Some participants proposed creative methods, and the best results are quite high, with precisions, recalls and F1-scores between 80% and 88% depending on the language pair. The participants' papers contain directions for further improvement of their methods and results. To alleviate the need for costly human evaluation, we designed a dataset in which known parallel sentence pairs have been inserted into monolingual corpora. Two risks were associated with this strategy. First, some participants might have tried to 'game' the task by attempting to discover the inserted sentences, for instance using plagiarism detection methods; we are glad that no participant seems to have done so.
Second, whereas we could control the inserted translation pairs and try to reduce the likelihood of occurrence of 'natural' translation pairs, we could not fully prevent some from occurring; human examination of sample results from the best runs suggests that 'natural' translation pairs add only a few percents to the inserted translation pairs, confirming the overall relevance of the BUCC 2017 Shared Task dataset and evaluation. The BUCC 2017 Shared Task dataset and evaluation program can be downloaded from the shared task's Web page. 5 urie Innovative Training Networks-European Joint doctorate (ITN-EJD) Under grant agreement No:676207 (MiRoR). Part of this work was supported by a Marie Curie Career Integration Grant within the 7th European Community Framework Programme.
Secondly, those platforms are cost effective and use either Bluetooth  [2]  or ZigBee  [3]  based radio transmissions to exchange data. In most cases, however, ZigBee is preferred because of its low energy consumption due to the possibility to put the radio in the sleep mode. As depicted in Figure  1 , such platforms are usually composed of sensors with embedded cameras and coordinators called sinks which collect pictures from the network and transmit them to remote databases servers. Sinks are generally linked to a computer or a smartphone/PDA that offer them different transmission technologies for Internet access (WiFi/3G/4G). Pictures on these databases servers can be accessed through simple browsers to allow monitoring staff to have a continuous view of elders environments. This paper presents our platform details that can be deployed in a common house, permitting the monitoring of several areas (rooms, kitchen, garden,.
Sensor nodes exchange data with the sink which has in addition to its ZigBee interface, a WiFi interface for Internet access without the need for a PC or a PDA. Information about taken pictures are stored in a remote database server and the pictures themselves are stored in a remote file server. This allows the web application we developed, to answer requests coming from end users asking for last pictures related to a specific monitored person. The web application gets information about stored pictures from the database server and then, requests them from the file server. Due to sensor's low calculating capabilities, we are not proposing any image processing made on sensors level. Any processing on pictures can be done at servers level which offer greater processing abilities.
foreach: CN in RoutingTable 3: Send request to CN 4: Wait for data from CN 5: if (receiving data) 6: while (receiving data) 7: Concatenate in SD file 8: Upload file's data to database 9: Upload file to FTP server 10: elseif (timer expires) 11: if (NbRetransmissions reached) 12: if (alternate route exists) 13: Swap it to routingTable 14: else 15: Retransmit request packet 16: Go to 5 At the reception of a request packet, a node verifies if it is addressed to it. If it isn't, it records the address of the node from which it got the request, this one will be the next hop towards the sink for data packets coming from the camera node specified as destination address in the request packet. Then, this intermediate node forwards the request packet to the next hop it has recorded during Signalisation Phase towards the destination camera node.