dataesr/scientific-paragraphs-categorization huggingface dataset:
Longer window sizes would be better suited for analyzing more general trends, at the scale of a shot, for instance. In the following, we will use the values computed using a 20frames window, since our editing annotations are at the level of the shot. As we suspected, the IOC values are relatively high (average of 4. 1 over the whole database), especially compared to IOC values in the static case; see for instance  Bruckert et al. (2019)  for IOC distributions on static datasets. This coroborates the findings of  Goldstein et al. (2007)  and  Breeden and Hanrahan (2017) , that viewers only attend to a small portion of the screen area. We also observe a disparity in IOC scores between the movies : the scene with the highest score on average, is the clip from The Shining (5. 76), and the lowest score on average is the clip from Armageddon (3.
• Kullback-Lieber Divergence (KLD ∈ [0, +∞[) between the two probability distributions represented by the saliency maps. In general, those results are quite low, compared to performances on non-cinematic video datasets (see for instance  Wang et al. (2019) ). This would indicate, in the case of deep-learning models, that either the training sets do not contain enough of videos with features specific to cinematic stimuli, or the deep neural networks cannot grasp the information from some of those features. Even though the best performing model is a dynamic one  (Zhang and Chen, 2019) , we observe that static models (DeepGaze II and MSINet) performances are quite close to those of dynamic models. This might support the latter hypothesis, that dynamic models fail to extract important temporal features.
Recent work from  Tangemann et al. (2020)  on the failure cases of saliency models in the context of dynamic stimuli also highlight this point, listing cases like appearing objects, movements or interactions between objects as some of the temporal causes of failure. Figure  9  shows an example from our database of such a failure case. It should be noted that all the deep learning models are trained on non-cinematic databases, with the exception of ACLNet, which include the Hollywood 2 dataset in its training base. However, this base is not well-fit to learn meaningful cinematographic features, as explained in Section 2. 3 In order to confirm this hypothesis, we retrained the ACLNet model using the same training procedure described in  Wang et al. (2018). For the static batches, we used the same dataset (SALICON  Huang et al.
Edition annotation and model performances We also studied how the two best dynamic models,  Zhang et al. (2020)  and ACLNet  (Wang et al. , 2019) , performed on our database, depending on shot, camera motion and camera angle characteristics. Table  4  shows the average results of the models depending on the annotation characteristics. Similarly to Subsection 5. 2, we performed one-way ANOVAs to ensure that results within each table would yield significant differences. In all cases, p-values were under 10 -5. As shown in Table  4  (a), it appears that saliency models perform relatively well on static scenes, or when the camera movement tracks an actor, or an object on screen. Performances are also quite good on shots including rack focuses, which was expected, as this is a very strong tool for the filmmaker to use to direct attention, and deep feature extractors distinguish very well blurry background from clear objects.
Conclusion and future work In this work, we introduced a new eye-tracking dataset dedicated to study visual attention deployment and eye fixations patterns on cinematographic stimuli. Alongside with the gaze points and saliency data, we provide annotations on several film-specific characteristics, such as camera motion, camera angles or shot size. These annotations allow us to explain a part of the causes of discrepancies between shots in terms of inter-observer visual congruency, and in terms of performances of saliency models. In particular, we highlight the conclusions of  Tangemann et al. (2020)  regarding failure cases of state-of-the-art visual attention models. Video stimuli sometimes contain a lot of non-static information, that, in some cases, is more important for directing attention than image-related spatial cues.
As these questions have been tackled from a psychological or cognitive point of view, they remain to be studied by the computer science part of the field, and to be included in visual attention models. This would greatly benefit multiple areas in the image processing field, like video compression for streaming, or automated video description. Furthermore, we hope that this data would help cinema scholars to quantify potential perceptual reasons to filmmaking conventions, assess continuity editing on sequences and hopefully improve models of automated edition  (Galvane et al. , 2015). Finally, developing automated tools to extract similar high-level cinematic information could be particularly of interest, both for the design of such tools, as it would give cues on the way to design better visual attention models on cinematographic data, but also with its outcome, as it would allow the provision of large-scale annotated cinematic databases, which would give a new -quantitativedimension to research on movie contents by cinema scholars.
Scores of two saliency models on the database, depending on hand-crafted editing features. Highest score for each metric and each model is bolded, lowest score is italicized. 532 0. 586 0. 549 0. 497 0. 510 0. 473 0. 520 0. 512 NSS 2. 596 2. 271 2. 689 2. 677 2. 497 2. 481 2. 255 2. 478 2. 543 Zhang et al. CC 0. 656 0. 607 0. 663 0. 645 0. 580 0. 615 0. 567 0. 628 0. 636 NSS 3. 320 2. 679 3. 099 3. 186 2. 889 3. 027 2. 733 3. 089 3. 221 (c) Scores depending on shot size.
The wide adoption of Electronic Health Records (EHR) in hospitals provides unique opportunities for high throughput phenotyping of patients. The phenotype extraction from narrative reports can be performed by using either dictionary-based or data-driven methods. We developed a hybrid pipeline using deep learning to enrich the UMLS Metathesaurus for automatic detection of phenotypes from EHRs. The pipeline was evaluated on a French database of patients with a rare disease characterized by skeletal abnormalities, Jeune syndrome. The results showed a 2. 5-fold improvement regarding the number of detected skeletal abnormalities compared to the baseline extraction using the standard release of UMLS. Our method can help enrich the coverage of the UMLS and improve phenotyping, especially for languages other than English.
g. , based on the Unified Medical Language System (UMLS) CUIs  [4]  which enables the easy reuse and comparison of the retrieved phenotypes and the conversion to other thesauri or languages. These methods generally reach better precision than recall  [2] , due to the presence of specific synonyms or variations of the terms in text. Moreover, the coverage of used thesauri can vary across languages, especially for rare phenotypes. To address these issues, nondictionary-based methods leveraging deep learning have been used to detect medical entities, such as BiLSTM-CRF  [5]  and PhenoTagger  [6] , which have been evaluated on medical articles. These developments have been part of a wider move toward deep learning in healthcare, due to the large availability of biomedical data  [7]. Regarding medical entity extraction, these methods do not provide the same normalization as thesaurus-based ones and their performance on clinical text must be analyzed.
Patient selection and extraction of phenotypes The Necker Children's Hospital is a French reference center for rare and undiagnosed diseases that hosts the Imagine Institute, a research center specialized in genetic diseases. Its clinical data warehouse (Dr. Warehouse  [8] ) contains EHRs of more than 800,000 patients. Dr. Warehouse enables the automatic extraction of medical entities from EHRs based on the UMLS  [4]. In addition, a research database is used to store structured research information from rare disease patients of Imagine Institute, including patient diagnoses. We selected all patients diagnosed with Jeune syndrome from the Imagine research database and limited ourselves to the patients followed up at Necker hospital, i. e. , with EHRs in Dr. Warehouse. UMLS extraction was performed by the high throughput phenotyping module of Dr.
Warehouse  [8]. The deep learning extraction was performed by applying a biGRU-CRF  [9]  extension to the method described in  [10]. This model is well suited for NER from noisy natural language text of EHRs due to its ability to remember information across different ranges of contextual dependencies  [11]. Each document was tokenized and fed as a string of fasttext word embeddings to the NER model. We reused an internal annotated dataset of 625 clinical reports in French from various departments at Necker hospital for model selection and training. These reports were manually annotated with spans corresponding to phenotypic entities (i. e. , observable physical or mental characteristics of the individuals, including both pathological and normal characteristics). The annotation provided 6928 phenotypes stemming from 2.
UMLS and biGRU-CRF extraction and evaluation The set of Jeune syndrome EHRs was randomly split into two datasets: training set (two thirds of the patients) and test set (the remaining third). Phenotypes were extracted from the training set using the UMLS extraction and the biGRU-CRF method. Phenotypes detected exclusively by the biGRU-CRF method were manually reviewed to verify whether they were skeletal abnormalities associated with Jeune syndrome and mapped to UMLS concepts. The list of skeletal abnormalities and their mappings to the UMLS were validated by three bone disease experts from Necker/Imagine (VCD, GB, CM). The enriched version of the UMLS integrating these new synonyms in French will be referred to as UMLS+ in the following. The last step consisted in extracting phenotypes from the test set with the dictionary-based method using standard UMLS and UMLS+ and comparing the results.
Training set and UMLS enrichment Starting from the research database, we identified 142 patients with Jeune syndrome. Among them, 35 patients were followed up at Necker, with an average of 22 documents per patient in Dr Warehouse (median 10). 23 patients out of 35 were randomly selected for the training set. These patients were associated with a total of 524 documents on which UMLS extraction and biGRU-CRF techniques were applied. The standard UMLS extraction identified 592 distinct terms (3387 occurrences) in the training set. Compared with this method, the biGRU-CRF method was able to detect 1036 additional distinct terms (1922 occurrences). The review of these additional French terms by clinical experts led to the identification of 119 skeletal abnormalities related to Jeune syndrome, among which some key phenotypes such as thoracic dystrophy, narrow thorax, and short ribs.
Acknowledgement This work was supported by the  French National Research Agency (ANR)  under the C' IL-LICO  project ( ANR-17-RHUS-0002 ), approved by the  French ethics and scientific committee  for health research (CESREES) (# 2201437 ), and as part of the  "Investissements d'avenir"  ( ANR-19-P3IA-0001 ) ( PRAIRIE 3IA Institute ).
Another parameter denoted depth relates to the lane connectivity: in a router of depth D, a packet can at most be routed on D lanes before leaving the router. For the performance exploration, we consider a 4 × 4-mesh network. We use a packet length of 10 flits, with flit size of 32 data bits in presented simulations. In our VHDL test benches, the processing blocks attached to the routers' local ports serve as both producers and consumers of packets. For each tested injection rate, the simulation runs until a stable average latency is reached, at which point the value is recorded, and the next simulation is executed. W L N S E W L N E S W L N E S C0 C1 C2 W L N S E W L N S E W L N E S C3 C4 Figure  13a  depicts the performance of different router configurations for uniform traffic patterns.
While we advance toward the revolutionary potential promised by quantum computing to simulate the quantum behavior of matter beyond today's capabilities,  [190]  research in materials science keeps exploring new theoretical frameworks and computational schemes scaffolding the progress of nanotechnology. A good example of this drive is the wide adoption of techniques from the growing literature in machine learning. [191] The interaction between these statistical methods and the natural sciences has proven useful in a variety of problems, of which some examples centered around chemistry and nanophotonics include classification tasks of large molecular databases,  [192]  rapid screening of candidate single-atom catalysts using predictive techniques generalizing from large datasets,  [193]  or solving inverse problems where we need to find a system delivering a target response.
[208, 209] t it is also important to remember that ML techniques can also be fruitfully applied in tandem with experimental data,  [210]  shortening the exploration of the space of parameters underscoring the synthesis of novel materials, facilitating the interpretation of material characterization datasets, or guiding the sampling of catalysts candidates. In a recent example of this latter possibility, Williams et al. employ ML methods to accelerate the discovery of efficient catalysts in the context of NH3 conversion. [211].
Acknowledgements B. P. acknowledges grant  ED431H 2020_08  from  Xunta de Galicia Government  and finantial support from  Spanish Ministerio de Ciencia e Innovación  under project  PID2020-119242RB-I00. M. C. -H. acknowledges financial support from the  CNRS Energy unit (Cellule Energie)  through the project  PEPS-CHEAP. L. V. B. acknowledges support from the  Spanish Ministerio de Economía y Competitividad  under Project  PID2020-118282RA-I00 ,  Xunta de Galicia (Centro singular de investigación de Galicia accreditation   2019-2022 ), the  European Union  ( European Regional Development Fund -ERDF)  and the  National Natural Science Foundation of China  (Project No. 12050410252 ). M. V. G. acknowledges support from the  Spanish Ministerio de Ciencia e Innovación  under grant  IJC2019-040291-I.
To overcome this problem, we propose a deep recurrent architecture RU-BiLSTM based on bidirectional LSTM (BiLSTM) coupled with word embedding and an attention mechanism for sentiment analysis of Roman Urdu. Our proposed model uses the bidirectional LSTM to preserve the context in both directions and the attention mechanism to concentrate on more important features. Eventually, the last dense softmax output layer is used to acquire the binary and ternary classification results. We empirically evaluated our model on two available datasets of Roman Urdu, i. e. , RUECD and RUSA-19. Our proposed model outperformed the baseline models on many grounds, and a significant improvement of 6% to 8% is achieved over baseline models.
Problem Statement In NLP, a Roman Urdu sentiment analysis has always been a challenging task owing to the colloquial nature of text written in Roman Urdu. While processing a text for analysis, creating a feature vector is an important step. A good feature vector would yield good results, and a poor feature vector will eventually trigger disastrous results. The traditional bag-of-words model or vector space model for feature extraction is not sufficient for dealing with such informal text. The authors of  [13]  claimed an accuracy of almost 60% on the Roman Urdu Dataset using the vector space model along with the TF-IDF weighting scheme with an SVM classifier, which should be ameliorated. Among the schemes for sentiment analysis, the n-gram statistical or rule-based methods are the most commonly used.
Significance of the Study Artificial neural networks (ANN) have brought about a revolution not only in computer vision and speech processing but also in the field of NLP  [14]. The state-of-the-art RNN, LSTM, and BiLSTM for Roman Urdu sentiment analysis is itself a significant study in assessing the power of neural networks. Prior to this, very few studies experimented with Roman Urdu text on neural nets. In contrast, our sequential modelling approach of LSTM and BiLSTM along with an attention mechanism is a novel approach to solving the Roman Urdu Sentiment Analysis puzzle. We empirically evaluated that our scheme outperformed all existing schemes in classifying Roman Urdu sentiments. For a better evaluation, we used two different Roman Urdu datasets with 26,824 and 10,015 number of documented instances in each dataset.
Roman Urdu being resource starved has no such pre-trained models or word embedding to be utilised for transfer learning or any other futuristic approach. Nonetheless, an ample amount of research has been conducted on many famous languages such as English, German, Chinese, and Arabic  [23] [24] [25] [26] [27]. In contrast, very limited research work has been conducted on South-Asian languages, more specifically on Roman Urdu  [7, 12, 13, 28]. The lack of language resources, such a benchmark dataset and pretrained neural embedding, has been a primary bottleneck in mitigating the sentiment analysis problem  [22]. Traditionally, the bag-of-words model or vector space model is used for sentence modeling and feature extraction and suffers from the curse of dimensionality  [15]. It was further argued that the CNNs and RNNs have emerged as two widely used architectures.
Hence, the LSTM was found to be much more reliable in capturing spatial and temporal dependencies  [29]. In contrast, the traditional feed-forward network fails to preserve the contextual information and cannot handle long-term dependencies. Owing to the high dimensionality of the sequential data (such as text), it may trigger a big challenge, and the solution to such spatial data is BiLSTM, which overcomes many long and short dependencies. Many researchers have utilised the fusion of both CNN and RNN for text classification. The authors of  [3]  have proposed an attention-based BiLSTM with a convolutional layer scheme called AC-BiLSTM for sentiment classification. Their proposed recipe uses convolutional layers to extract higher level features, which are further fed into LSTM hidden units to learn long-term dependencies.
The learning classifiers were trained on unigrams and weighted unigrams. It was identified that machine learning algorithms work well on weighted unigrams and that SVM has shown better performances  [33]. Recently, a personalised sentiment classification approach that considers additional attributes such as product information and user-text was introduced  [34]. To extract implicit information, three interactive attribute encoders were used in addition to the local text encoder: user-product, user-text, and product-text encoders. They empirically evaluated three different datasets: IMDB, Yelp and Amazon Reviews. Then, they claimed to have achieved a significant improvement in performance for the proposed approach. In another research study, the authors focused on a sentence-level sentiment analysis.
They proposed a sentence-to-sentence attention network (S2SAN)  [35]. They were of the view that a word representation approach for sentiment analysis overlooks the importance of each sentence, which can be dealt with using a sentence-level attention mechanism. It was identified that deep learning classifiers such as CNN, RNN, and LSTM embedded within a sentence attention framework yields good results. Elfaik et al. [36]  has proposed Bidirectional LSTM for Arabic sentiment analyses. Arabic being a morphologically rich and complex language has presented challenges for researchers to undertake sentiment analyses. The experiments were conducted on six benchmark Arabic datasets, which revealed that the proposed model outperformed the state-of-the art machine learning and deep learning models.
Other research have used unsupervised and lexicon-based algorithms to match target words in a lexical knowledge base (KB) with seed words in a sentiment lexicon but have run into issues with affective concept coverage and connectedness. Fares et al. [38]  has introduced an LSA-based unsupervised approach LISA, a knowledge graph-based LSA framework that is unsupervised at the word level. It computes and propagates affective scores in a lexicalaffective graph (LAG) formed by linking a conventional lexical KB such as WordNet with a trustworthy affect KB such as WordNet-Affect Hierarchy using different variations of shortest path graph navigation algorithms. Sequence-to-sequence deep learning models are a class of Recurrent Neural Networks (RNNs) that are used for different language problems such as machine translation, text summarisation, question answering, and text generation.
An aspect-level sentiment analysis approach was introduced, titled as M-IAN, to capture the interactions between target and context using a deep interactive memory network  [42]. Word representation plays a vital role in the text classification task. In a series of experiments on benchmark datasets using CNN with word2vec, Kim achieved a remarkable accuracy  [16]. It was identified that, with a little fine-tuning of the CNN hyperparameters, a remarkable performance can be achieved. Kim was of the view that pre-trained word vectors could better help deep networks improve their performance. A recent study on aspect-based sentiment analysis has proposed a deep learning model NA-DLSTM. They deliberated that sentiment analysis and aspect-based sentiment analysis can be differentiated in a way that the former considers the whole document whereas the later analyses and categorises the text into various aspects and assigns the polarity to each aspect.
The study was aimed at developing a context-aware aspect-based SA model, experimenting on the Rest14, Rest15, and Rest16 dataset  [43]. An important task while performing the sentiment analysis is feature selection, which suffers in the presence of uneven class distribution of the datasets  [44, 45]. In text classification, usually two classes are involved: the positive class (the class of interest) and the negative class, which is mostly over-represented. This over-representation is known as the class imbalance problem  [46]. This imbalanced class distribution allows for the redundant, noisy and erroneous features to gain more weight. An earlier study of imbalanced data by  [42, 47, 48]  also identified that imbalanced class distribution poses a significant challenge to the classifier's performance.
To overcome this problem, they proposed the Adaboost algorithm. However, this uneven distribution was still a challenge while selecting the features. Both of our Roman Urdu datasets also face the same issue of imbalanced class distribution, which triggers a performance downgrade. There exists no pre-trained word vectors for Roman Urdu. Assessing the need to enrich this resource-starved language, we introduced for the very first time three different word embedding schemes: word2vec, Glove, and FastText.
An Insight into Roman Urdu Sentiment Analysis In a systematic literature review (SLR) on Roman Urdu sentiment analysis, it was identified that the Urdu language lacks standardised datasets for sentiment analysis, which is critical  [49]. Furthermore, pre-processing and feature selection mechanisms should be designed in accordance with Urdu language's peculiarities. In a recent study on lexical normalisation on Roman Urdu, a phonetic algorithm-based approach TERUN (Transliteration based Encoding for Roman Urdu text Normalisation) was proposed  [50]. TERUN uses features of Roman Hindi/Urdu linguistics to convert lexically variable terms to their canonical forms. Transliteration-based encoder, filter module, and hash code ranker are the three interconnected modules. For a single Roman Hindi/Urdu word, the encoder creates all possible hash-codes.