dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We have introduced the notion of flow in citation networks, proposed a measure the ascending flow, discussed its implementation and experimented with it in the arXiv HEP-Th dataset. This notion of flow allowed us to propose the new approach of the citation network as a multiplex DAG for which we offered three measures -the aggregated, sum and selective flows -with implementation and experiments on the same dataset. We also leveraged visualization to explore our results and discovered interesting insights. Although our study does not hold for an evaluation for which a comparison with many other metrics and regression will be necessary, we still have set and validated the basis of our framework. Not being high-energy theoretical physicists ourselves, it remains somewhat difficult to directly interpret the results, nonetheless exploration of our results showed interesting insights.
It will be important in the future to carefully curate controlled datasets for which we can expect results and apply former validation. As such, it would be interesting to examine the insights we could extract from a citation network inherited from all publications of a conference venue; to explore neighborhoods of important works such as surveys; to suggest new relevant works from a set of publications; or to analyze the genesis of two merging domains. We wish to extend our study to other databases, such as DBLP or web of science, and bring the analysis to the authors themselves -although ambiguity in author names is quite challenging. It will be interesting to observe the influence of Nobel prizes for example, find their most influent work and their own influence. Finally, this framework is not limited to citation networks, but can apply to many other type of transferred information: among our future works is the application to the analysis of news documents.
Endnotes 1 Available at: http://snap. stanford. edu/data/cit-HepTh. html. 2 The Coupling of Yang-Mills to Extended Objects  (Dixon et al. 1992) , by Dixon, Duff and Sezgin published on arXiv in 1992. And it has well deserved another citation! 3 By "flattening", we mean the process of projecting the multiplex network such that one edge only is shared between two nodes in order to obtain a monoplex version of the multiplex network. 4 https://github. com/renoust/Detangler/tree/demo.
Availability of data and materials The original citation data are publicly available from http://snap. stanford. edu/data/cit-HepTh. html. Our code and implementation are publicly available from https://github. com/Azzaare/multiplex-carbonara. Processed network data are available under the Tulip. tlpx format at https://github. com/Azzaare/multiplex-carbonara/tree/master/network_data. Individual subgraph data to be explored with Detangler are available at https://github. com/Azzaare/multiplex-carbonara/ tree/master/detangler_data. Tulip is available at http://tulip. labri. fr/. Detangler is available at https://github. com/renoust/ Detangler/tree/demo, with the online tool at http://164. 132. 58. 138:1723/. All URLs were last accessed on 13/03/2017.
All the computations presented in this paper were performed using the Cactus platform of the CIMENT infrastructure (https:// ciment. ujf-grenoble. fr), which is supported by  Region Rhone-Alpes  ( GRANT CPER07-13 , support. MD and JE thank  ANR-12-BS09-0020-01  " Transmig " and  ANR-11-LABX-0030  " Tec21 ", and are members of GDR 3570 MecaBio and GDR 3070 CellTiss of CNRS. JE thanks the  Isaac Newton Institute for Mathematical Sciences  for its hospitality during the programme " Coupling Geometric PDEs with Physics for Cell Morphology, Motility and Pattern Formation " supported by  EPSRC  Grant Number  EP/K032208/1. CML, GBB and BS were supported by  Wellcome Trust   Investigator Award   099234/Z/12/Z  to BS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Of note, there is underrepresentation of fungal sequences in annotated reference sequence databases. As of July 2017, the National Center for Biotechnology Information (NCBI) database (  22 ) listed 1,069 complete fungal genomes compared to 14,249 complete bacterial genomes, and so fungi may be underdetected by shotgun sequencing efforts. In addition, fungal cells are more than 100-fold larger than typical bacterial cells; thus, they provide a substantial mass of biomaterial and unique metabolic functions to the microbiota as well as surface area for host-microbe interactions. Although they represent a small component of the microbiota, fungal communities are significant in that they potentially serve as a reservoir for pathogens as well as for keystone species with critical roles in maintaining the function of the gut microbiome  (23).
(a) Relative abundance plots of bacterial taxa in mouse feces. Taxa were identified to 97% similarity using the Greengenes reference database, as described in Materials and Methods. (b) Relative abundance plots of fungal taxa in mouse feces. Taxa were identified to the species level using the UNITE reference database, as described in Materials and Methods. For the results of statistical comparisons of taxon abundances between diets, see Fig. S2  and S4 in the supplemental material. this study contributed to mouse fungal microbiomes, fungal DNA was extracted from the high-fat and standard chows and analyzed using qPCR and sequencing. By qPCR analysis, the standard chow contained a much larger amount of fungal DNA than did the high-fat chow (mean quantification cycle [C q ] values of 22 and 36, respectively).
5 ). In mice fed standard chow, there were 8 significant fungal-bacterial coabundance correlations: 7 were positive, and 1 was negative (Fig. 5 ; see also Table  S7  at http://galelab. umn. edu/msphere-supplemental-material). In mice fed the high-fat diet, there was a reduction in coabundance correlations, with only 3 being observed (one positive and two negative) (Fig. 5b ; see also Table  S8  at http:// galelab. umn. edu/msphere-supplemental-material). The specific interkingdom taxon correlations completely differed between the diets. Altogether, these data support the idea that interkingdom interactions exist between bacteria and fungi in the gut and that these relationships depend upon diet. Fungal taxa correlate with bacterial KEGG modules. Inference of fungal microbiome function from sequence data is not technically possible due to the paucity of fungal sequences in current databases, complexities of fungal taxonomy calling, and variability in functions expressed by fungi depending on specific environmental conditions, compared to bacterial sequences.
The vO 2 measurement was recorded every 4 min for 2 days and normalized to body weight using Oxymax software (Columbus Instrument, Columbus, OH). (iii) Metabolic biomarker assays. Blood samples were collected from animals at the time of sacrifice (~24 weeks of age), and metabolic biomarker levels were measured using commercially available enzyme-linked immunosorbent assay (ELISA) kits for plasma insulin and adiponectin (EMD Millipore, Billerica, MA) and a fluorimetric assay for total serum cholesterol (Cayman Chemical, Ann Arbor, MI). The L-type TG M kit and NEFA-HR(2) kit (Wako Diagnostics, Richmond, VA), which utilize an enzymatic colorimetric method, were used to measure serum triglycerides and free fatty acids, respectively. Fungal ITS2 amplicon generation and sequencing. DNA was extracted from fecal pellets obtained from mice during week 16 of life and from three separate samples of both the high-fat diet and the standard mouse chow using the PowerSoil DNA isolation kit (Mo Bio Laboratories, Inc.
DNA libraries were generated from the resulting amplicons using the Illumina TruSeq Nano kit (Illumina, San Diego, CA), and amplicons were then sequenced by the Illumina MiSeq platform using the 2-by 300-bp paired-end V3 kit (Illumina). Microbial community analysis. (i) Fungi. Fungal sequences were paired, filtered for quality, and aligned using an experimental new aligner, BURST  (52) , which was developed to perform optimal exhaustive alignment and interpolate the taxonomy annotations for all reference database matches for each query read. For instance, if a given query read matched with a similar alignment score to 5 references, all of which have the same genus name but conflicting species names, BURST would annotate that read at the genus level without overconfidently committing to a single species for which there was conflicting sequence evidence.
To circumvent issues related to the uncurated nature of the UNITE database (53, 54), ITS2 sequences were aligned to the RefSeq v81 Targeted Locus Project ITS region database  (53) , which contains a highly curated set of approximately 5,500 full-length fungal ITS markers that have been sequenced and characterized using a consistent protocol. Replicate samples were pooled bioinformatically prior to analyses to provide a more robust representation of taxa within the samples  (15). There was a mean of 19,470 fungal sequences per sample, with a range of 2,435 to 71,050 reads per sample. The taxonomy table was rarefied to 2,435 sequences to perform diversity analyses. Alpha-diversity analyses were performed using Shannon, Chao1, and unique observed species indices. Beta-diversity analyses were performed using Bray-Curtis distance metrics, as implemented in Quantitative Insights Into Microbial Ecology 1.
8. 0 (QIIME  [51] ). (ii) Bacteria. The sequence data were processed in QIIME. Data were quality filtered and demultiplexed using default parameters. Sequences were assigned to 97% identity operational taxonomic units (OTUs) by comparison to the Greengenes reference database 13_8 (55) using UCLUST  (56). The OTU table was then rarefied to 90,851 sequences to perform diversity analyses. Alpha-diversity analyses were performed using Shannon and phylogenetic diversity indices. Beta-diversity analyses were performed using unweighted and weighted UniFrac (57) distance metrics, as implemented in QIIME. The relative abundances of 558 level 3 KEGG modules were predicted from the OTU table using BugBase  (28). (iii) Statistical analyses. Principal-component analysis (PCoA), permutational analysis of variance (PERMANOVA) tests, and Procrustes analysis were performed in QIIME.
Comparison of taxonomic count data and KEGG modules between fecal samples collected from mice on a high-fat diet and those collected from mice on a normal diet was performed using the Mann-Whitney U test with false discovery rate (FDR) correction for multiple testing. Box plots, bee swarm plots, heat maps, randomForest analyses, and two-dimensional PCoA plots were generated using R  (58). We also performed Spearman correlations between relative abundances of fungal and bacterial profiles and between relative abundances of fungal taxa and bacterial KEGG modules and developed a permutation-based test of the Spearman correlation test statistic with 1,000 permutations to test for conserved correlations between diets and BugBase phenotypes using R. We plotted a network of fungal and bacterial pairs that significantly correlated with an FDR-corrected P value of Ͻ0.
05 with Cytoscape  (59). qPCR. Quantitative real-time PCR (qPCR) analysis was performed on a limited number of samples as an additional method to characterize fungal presence in the samples. All fecal DNA samples were subjected to qPCR (Roche LightCycler 480; Roche, Indianapolis, IN) with universal fungal primers. In addition, a subset of five fecal samples as well as chow samples was subjected to qPCR using fungal species-specific primers. qPCR protocols were performed as previously described by our research group  (15)  and included negative (no-template) controls to study the potential for contamination of samples with exogenous fungal DNA. Availability of data. Sequencing results are available in the Sequence Read Archive (SRA) database at NCBI under BioProject ID PRJNA353013. R scripts are posted at http://galelab.
ACKNOWLEDGMENTS We are grateful to  Sara Gonia  for technical assistance and helpful discussions during preparation of the manuscript. E. M. was supported by the  Robert Tournut award of the French National Society of Gastroenterology. This work was further supported, in part, by  NIH   DK54733  and  DK60521  to L. -N. W. and a  Minnesota Viking Children's Research Fund  award to C. A. G.
INTRODUCTION Emotion recognition is an active research topic in the affective computing community. During the last decade, emotion recognition systems have been integrated in a number of applications across a growing number of domain fields such as cognitive science  [31] , clinical diagnosis  [15] , entertainment  [38]  and human-machine interaction  [3]. Automatic emotion analysis and recognition in realworld videos (i. e. in the wild) is nevertheless still an open challenge in computer vision. One fundamental limiting factor is that there is almost no large dataset with real-world facial expressions available for emotion recognition. Other challenging factors include head pose variation, complex facial expression variations, different illumination conditions and face occlusion. Recent achievements in the field are based on the use of data coming from multiple modalities, such as facial and vocal expressions.
Data Preprocessing Video clips from emotion recognition datasets are usually collected from classic movies and TV reality shows, so most of frames contain irrelevant or misleading information, like background objects and background motion. Therefore, it is beneficial to pre-process the original video frames in order to limit this effect. Indeed, we extract all faces from each frame of the input video clip, and retain only the face bounding boxes, discarding all the rest in a frame. For the face detection and extraction phase, we use a cascaded convolutional neural network  [46]  in which faces are detected by means of a multitask convolutional network which jointly detects facial landmarks and predicts the face bounding box. If more than one face is detected, we only use the crop of the biggest one as input to the model.
This network is trained using every extracted face from the challenge dataset and every image of the FER-2013 dataset  [13]  (more details regarding the datasets are available in Section 4. 1). Images are preprocessed accordingly to the chosen open-source implementation of the network  1. Note that only the last convolutional and fully connected layers are trained from scratch. C3D Branch. In this branch, the C3D network  [43]  is used as feature extractor. The network takes 16 frames of size 112 × 112 × 3 as input. The output of the Pool5 block (of size 4 × 4 × 512) is given as input to a small neural network composed by a max pooling layer of size 2×2 and stride 2, a convolutional layer with 256 filters of size 2 × 2, a fully connected network with 256 units and a softmax layer of 7 classes.
Deep Acoustic Features Extraction In the audio branch, the SoundNet network  [2]  is used as feature extractor. The output of the conv4 block (of size 22 × 128) is given as input to a network defined as follows. The first layer is a 1D convolutional layer with 512 filters of size 4, applied with a stride of 4. The size of the output is 6 × 512. The six feature vectors of size 512 can be seen as the compression of the audio temporal input, therefore they still contain temporal information. Based on that, the six feature vectors are given as input of an LSTM  [20]  layer with two levels and 128 hidden units. This layer is followed by a softmax layer of 7 classes. The network is trained using the challenge dataset and part of the eNTERFACE dataset  [32]  (more details regarding the datasets are available in Section 4.
Training The training process is composed by two phases. During the first phase, the last layers of the three branches of our architecture are trained separately. Then, the multimodal fusion network is trained using the trained branches, without the softmax layer, as feature extractors. This approach allows us to use additional datasets during the training of the single branches, obtaining more robust and generalizing networks as feature extractors. The categorical cross-entropy loss function on the seven classes of the challenge dataset is used to train all the networks of the architecture except the multimodal fusion of the Submission 4. The fusion network related to the last submission is trained using a weighted version of the categorical cross-entropy loss function. In order to increase the importance of the most frequent classes and reduce the importance of the less frequent ones, the standard loss value is multiplied by a regularizing parameter based on the distribution of the seven classes in the training set.
Datasets We trained our networks with different emotion datasets, evaluating them on the challenge dataset only. Acted Facial Expressions in the Wild (AFEW). The Acted Facial Expressions in the Wild (AFEW) dataset  [9]  (2017 edition) is the dataset of the Emotion Recognition in the Wild (EmotiW) challenge. It is composed by 1809 video clips extracted from movies and, since 2016, TV series. Every clip is annotated with one of seven emotions (Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral), but only the annotations of the training and validation sets are publicly available. Some statistics about the dataset are available in Table  1. In Figure  2 , some frames from the dataset and the corresponding cropped faces are shown. The dataset is used for both training and evaluation of all the branches and the multimodal fusion.
Facial Expression Recognition 2013 (FER-2013). The Facial Expression Recognition 2013 (FER-2013) dataset  [13]  has been created for the Facial Expression Recognition Challenge. 35, 887 grayscale images have been crawled on the web and annotated with one of seven emotions (Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral). These additional images increase the accuracy of the CNN branch when used during the training. eNTERFACE. The eNTERFACE dataset  [32]  consists of 1166 video clips annotated with one of the six basic emotions (Angry, Disgust, Fear, Happy, Sad and Surprise). The clips are recorded in constrained environments and contain both audio and video data. This dataset is used during the training of the Audio branch of our architecture, decreasing the over-fitting.
The only exception is the class Fear, mainly confused with Angry, Neutral, and Sad. Interestingly, while analyzing results on the validation data we observed that the CNN branch correctly classify about every emotion with an acceptable accuracy, the C3D branch is unable to classify the classes Disgusted, Fear, and Surprise in most of the cases whereas the Audio branch never correctly classify the classes Disgusted and Surprise. Additional experiments were made on the validation set to investigate the fine-tuning of the pre-trained networks (FaceNet, C3D,  SoundNet), but they resulted in an early over-fitting of the models. Indeed, over-fitting has been a major issue in most of the experiments performed in this work. We think this is mainly caused by the limited size of the available datasets regarding multimodal emotion recognition.
Challenge Submissions: Results on Test Set. In order to evaluate the performance of our different approaches/models on the challenge test set, we submitted 4 runs to EmotiW 2017 challenge. In this paper, we present only the three best submissions (1, 3 and 4). In particular, submission 1 corresponds to a preliminary version of our architecture: it contains the CNN branch and the Audio branch only and it is trained with the standard categorical crossentropy loss function (Eq. (  3 )). Differently, both submission 3 and submission 4 correspond to the architecture described in Section 3. The standard categorical cross-entropy loss function (Eq. (  3 )) is used in the first case while its weighted version (Eq. (  4 )) is used in the second one. To increase the training data while keeping a stopping condition, the validation set of the AFEW dataset was split in five folds and the models are trained five times, following the k-fold cross validation technique.
The folds were created maintaining the train and the validation fold subject-independent. Submission 2 attempted to keep emotion-balance instead of subject independence while training the same architecture as submission 1, but poor results were obtained and hence are not reported. The results of our submissions are presented in Table  3. The second column of the table contains the averaged accuracy on the five validation folds, while the third one contains the results of our submissions on the test set. Looking at Figure  3  and Table  3 , it can be noticed that the multimodal fusion network trained with the weighted loss (Submission 4) performs better on the test set, while the model trained with the standard loss performs better on the five validation folds. This counter-intuitive behaviour is presumably attributable to the different class distribution of the test set of the AFEW dataset compared to the train and validation set of the same dataset.
CONCLUSION We proposed a multimodal deep learning framework for emotion recognition in video that participated to the audio-video based subchallenge of the Emotion Recognition in the Wild 2017 challenge. Our approach combines visual, temporal and audio information using neural network-based architectures only. Notwithstanding the small amount of labelled data regarding the emotion recognition in video, the proposed method outperforms the challenge baselines of 38. 87% and 40. 47% obtaining an accuracy of 50. 39% and 49. 92% on the validation and the test dataset respectively. In the future, making use of larger annotated datasets, we are planning to train the entire architecture in one step and to fine-tune the pre-trained audio and visual models to make the extracted features more domain specific.
Database Search and LOX Sequence Retrieval Protein and CDS sequences of E. siliculosus V2016 version were downloaded from the website http://bioinformatics. psb. ugent. be/orcae/overview/Ectsi. Those of S. japonica were downloaded from NCBI under the accession code JXRI00000000. Sequences of C. okamuranus were downloaded from http://marinegenomics. oist. jp/algae/. Other genomes were downloaded from the following sites: green plants, Phytozome V12. 0 https://phytozome. jgi. doe. gov/pz/portal. html. Emiliania huxleyi, oomycetes, http://genome. jgi. doe. gov/ pages/tree-of-life. jsf. Other genes were downloaded at NCBI https://www. ncbi. nlm. nih. gov/. The LOX domain PF00305 was downloaded from the Pfam website http://pfam. xfam. org/ (Finn et al. , 2015). HMMER was used to search for this domain in the predicted proteins of each species.
Phylogenetic Analysis Sequence alignments were performed on both the full-length proteins and the conserved PF00305 domain using MUSCLE embedded in MEGA 7. 0  (Kumar et al. , 2016). The maximum likelihood (ML) and Neighbor-joining (NJ) phylogenetic trees were constructed with best substitution models and rate parameter WAG + G + I found by MEGA 7. 0. Bootstrap with 1000 bootstrap replicates was performed to obtain the confidence support level. Bayesian inference of phylogeny was carried out using BEAST2  (Bouckaert et al. , 2014). Analyses were run for 100 million generations, with sampling every 10000th generation using the same substitution model as above and Yule Model as a prior. The first 10000 samples were discarded as burn-in.