dataesr/scientific-paragraphs-categorization huggingface dataset:
Building a Hexagonal AID Dataset After resizing the images to 64x64 pixels, the dataset is transformed to a hexagonal one, as shown fig. 8 , in the same way as in section 5. 2. 1. Then the images are standardized. Figure  8 : Example of an image from AID dataset resized to 64x64 pixels and resampled to hexagonal grid. The network considered in this experiment is still a ResNet architecture but adapted to this specific dataset. One follows the setup proposed in section 5. 2 of  Hoogeboom et al. 2018. Three networks have been implemented and trained in the same way described in section 5. 2. 2, with the number of features per layer described in table 3.
Results As shown in table 4, all three networks with hexagonal convolutions and square convolutions do not exhibit a significant difference in performances on the AID dataset. Again, no accuracy loss is observed in the hexagonal processing case study despite the rough image re-sampling. However, unlike on the CIFAR-10 experiment, we don't observe a better accuracy of the model with hexagonal kernels, as emphasized in  (Hoogeboom et al. , 2018). This paper introduces indexed convolution and pooling operators for images presenting pixels arranged in non-Cartesian lattices. These operators have been validated on standard images as well as on the special case of hexagonal lattice images, exhibiting similar performances as standard convolutions and therefore showing that the indexed convolution works as expected.
No additional pre-processing of the image is then required to apply convolution and pooling kernels. However, the current implementation in Python shows a decrease in computing performances compared to the convolution method implemented in Pytorch. We have observed an increase of RAM usage of factors varying between 1 and 3 and training times of factors varying between 4 and 8 on GPU (depending on the GPU model), of factor 1. 5 on CPU (but slightly faster than masked convolutions on CPU) depending on the network used. These drawbacks are actually related to the use of un-optimized codes and work is carried out to fix this by the use of optimized CUDA and C++ implementations. As a future work, we will use the indexed operations for the analysis of hexagonal grid images of CTA. We also plan to experiment with arbitrary kernels, which are another benefit of the indexed operations, for the convolution (e.
In respect to technologies specifically designed for teaching, particular software devices have been designed on the basis of didactic investigations. They thus appear as a paradigm of connection between fundamental research, teaching development and empirical validation of ICT didactic tools  (Chaachoua, 2015). The first one is Aplusix,  22  a micro world especially designed for the practice of elementary algebra that remains very close to the students' paper and pencil manipulations, while providing feed-back based on a detailed epistemological and didactic analysis of potential learning processes  (Trgalová & Chaachoua, 2009). Aplusix is based on a model of reasoning by equivalence between two algebraic expressions which is defined by the fact that they have the same denotation (Arzarello, Bazzini, & Chiappini, 2001).
LL-Net was evaluated for content retrieval by means of a computer simulation. Kovacević et al. also propose a hierarchical tree-based P2P overlay called Globase. KOM  [22]. They focus on a world-wide spanning network, which is divided into non-overlapping rectangular zones. Simulation results show that Globase. KOM achieves full retrievability of area searches and a short response time. Wang et al. develop a P2P mobile navigation system to guide visitors in a flora exposition with 3D renders  [23] ,  [24]. P2P technology is used for clustering neighboring users who have similar interests and for downloading data from them. They also validate faster transmission rates of the proposed system by computer simulation, assuming all devices are connected to Wi-Fi. The conventional location-based P2P systems mentioned above still suffer from certain problems.
The server provides a binding response that consists of the IP address and port number, as observed from the server's perspective; • The new peer then sends a registration request to the signaling server. The request message contains the peer's IP address and port number resolved by the STUN server. The server allocates a unique identifier to the peer (peer ID) and registers these three types data in the database. The server also sends back the peer ID to the peer as a response; • The peer sends a join message to the L-tracker. The message includes the peer ID and LI that is obtained by the GPS module. The L-tracker then registers them in the database and replies with an acknowledgment. This step will be periodically repeated for updating LI at intervals of T int. Establishment of P2P connection between peers.
If there is a particular type of NAT between two peers, the connection cannot be established by the above sequence. In this case, the peers try to connect through a TURN server  [26]. Moreover, the P2P connection process is executed after each periodic repetition join messages sent to L-tracker. The interval time is thus T int. The process of leaving G-LocON. Figure  4  shows the communication process when a peer leaves the G-LocON overlay network. The sequence of the leaving process is described as: • The peer first closes all the P2P connections with its neighbor peers, by sending connection closing messages; • The peer sends a leave message to the L-tracker. The L-tracker then deletes relevant information from the database; • The peer sends a peer unregistration request to the signaling server.
The server then deletes relevant information from the database. Avoidance of multiple P2P connections. If two peers attempt, virtually simultaneously, to initiate P2P connections with each other, multiple connections may be established occasionally. We therefore prepare a process to avoid such multiple connections. When a peer detects a new P2P connection a neighbor peer, peer checks if another connection with the same peer ID exists in the list of P2P connections. If it does, the new connection is immediately closed. Otherwise, the list is updated by adding information about the new connection.
Implementation We implemented the G-LocON framework to establish geolocation-based P2P overlay networks. The L-tracker was developed on NIFCLOUD, a public cloud computing service provided by Fujitsu Cloud Technologies  [27]. We also used SkyWay API, a WebRTC platform provided by NTT Communications  [28]. SkyWay also offers the STUN, TURN, and signaling services. The mobile software was developed with the use of Android Studio with NIFCLOUD and SkyWay APIs. Asus ZenFone 3 / 3 Laser / 2 Laser, LG Nexus 5 / 5X, Samsung Galaxy S6, and Sony Xperia ZL2 were used as Android-based smartphones. In the application developed, each peer obtains its LI from the GPS module every second, as well as registers and updates LI to the L-tracker every five seconds: T int = 5 s. After establishing P2P connections with neighbor peers, each peer tries to share and send LI to its neighbors as soon as LI has been updated, i.
Experimental Settings To evaluate the performance of G-LocON, we performed experiments to form a P2P overlay network comprising several Android smartphones operating in the real-world environment. In the experiments, we focused primarily on communication and processing delays. Here, t l , t s , and t r denote the times when a peer obtains its LI from the GPS module, when the peer starts sending LI to each neighbor peer, and when the neighbor peer receives LI, respectively. These times were recorded in each smartphone by relying on the GPS signal and its internal clock that had been adjusted beforehand by the network time protocol (NTP). T send = t r -t s represents the transmission delay required for a peer to send its LI to each neighbor peer. Meanwhile, T total = t rt l represents the total delay time from when a peer obtains its LI to when LI arrives at each neighbor.
Application Design To confirm the potential of the proposed G-LocON framework, we developed practical software for Android smart-phones. We believe that G-LocON may be applied in intervehicular or in vehicle-to-pedestrian communications. In the developed application, assuming that both drivers and pedestrians use their own smartphones, the locations and moving speeds of neighbor peers present in the focusing area are shown on a peripheral street map. The principal operation sequence of the developed software is based on the processes explained in Subsection 3. 2, as described below: 1. A peer first joins the G-LocON overlay network. The joining process is repeated every five seconds: T int = 5 s; 2. The peer sends a query message to the L-tracker to discover neighbor peers, and then obtains the list of neighbor peers in the circular focusing area whose radius equals 50 m; 3.
The peer sets up P2P connections with its neighbors. They exchange their actual locations and moving velocities, relying on P2P communication, every second. The moving velocity of peer i, denoted by v v v i (t), can be calculated as the difference between current and previous position vectors, v v v i (t) = P P P i (t) -P P P i (t -∆t) = x i (t) -x i (t -∆t), y i (t) -y i (t -∆t) , where P P P i (t) = (x i (t), y i (t)) represents position of peer i's at time t. ∆t represents the interval in which LI is obtained the GPS module, and ∆t = 1 s. 4. The peer's current location and circular focusing area are displayed on the peripheral map by relying on the Google map API. All neighbor peers are plotted concurrently as pins on the map. If the moving speed of a neighbor peer is v th faster than own speed, the color of pin becomes red to alert the user about a moving vehicle approaching in the vicinity.
Conclusions In this paper, we proposed a G-LocON framework to establish a P2P-based geolocation-oriented network. By using the proposed scheme, each mobile device can easily discover its neighbors around and may then form a G-LocON overlay network. We firstly developed a primitive Android application to implement the G-LocON framework by utilizing public cloud computing and WebRTC platform services. The results obtained from the evaluation experiments can be summarized as follows: • The average transmission delay between peers, represented as T send , is of the order of tens of milliseconds, and gradually increases with the number of neighbor peers; • The average total delay from the moment a peer obtains its LI from the GPS module to the moment LI arrives at each neighbor, represented as T total , is of the order of hundreds of milliseconds, and gradually increases with the number of neighbor peers; • When peers obtain the updated list of neighbor peers from the L-tracker, the total delay becomes much longer than that without the update.
Overview MuSCA is a generic, functional structural plant model (FSPM)  (Vos et al. 2010)  representing the carbon allocation and organ growth of a plant at different, user-defined, spatial scales, while taking into account competition and distances between carbon sinks and supplies present on the plant (Fig. 1 ). The model has a modular architecture and each module is generalized so that only a few of them are specific to a given cultivar or species (Table  1 , Table  2 ). The model is integrated in the open-source OpenAlea environment  (Pradal et al. 2008 (Pradal et al. , 2015) )  and implemented in the Python language. The model reuses existing OpenAlea components, such as the MTG dynamic data-structure  (Godin and Caraglio 1998 ; https://github. com/openalea/mtg), used to represent the multiscale topology of plant structures, the PlantGL library  (Pradal et al.
The creation of new metamers should be included in order to allow for the simulation of plant growth during elongation and growth periods (on not-fixed structures). In addition, given the influence of pruning on the distribution and emergence of carbon sinks, the implementation of a module describing reactions to this management practice (e. g. DeJong et al. 2012)  would significantly extend the applicability of the model to a broader range of real cases. Further, a more detailed description of the root would be the starting point to investigate water and nutrient limitations at the soil interface. Regarding the use of MuSCA in the larger context, its generality can ease its adaptation to different species. In addition, the modular implementation of MuSCA in the OpenAlea environment facilitates the integration with other, previously developed, models, as it was the case for the connection with the MAppleT  (Costes et al.
ACKNOWLEDGEMENTS We acknowledge  Jerome Ngao  and  Marc Saudreau  from  UMR PIAF, INRA at Clermont-Ferrand  respectively for their help in linking RATP to MAppleT and in linking MTG and RATP, in the OpenAlea platform. F. Boudon  from  UMR AGAP, Cirad at Montpellier , for some tips on Python coding. The authors thank also  Roberto Zampedri  and  Mr Mauro Cavagna  for field assistance,  Maddalena Campi  for useful discussions, and  Gareth Linsmith  for supervision on the English language and kinship.
D-WEE: a dynamic thermal desorption module for SolEdge2D-EIRENE In this section, the D-WEE module developed for the SolEdge2D-EIRENE code package is introduced. SolEdge2D-EIRENE is able to give the plasma parameters (density, particle flux density and temperature) along the complex Vacuum Vessel (VV) wall. In the following, the s symbol denotes the curvilinear coordinate along this wall (cf. Fig. 1  for the definition of this coordinate in the case of the JET tokamak), while the x symbol denotes the depth of the material (transverse to s ).
Acknowledgements This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. This work was granted access to the  HPC resources of Aix-Marseille Université  financed by the project  Equip@Meso  ( ANR10-10-EQPX-29-01 ) of the program ‹‹ Investissements d'Avenir ›› supervised by the  Agence Nationale de la Recherche.
Due to the recent advancement in genomic sequencing technologies, the number of protein sequences in public databases is growing exponentially. The UniProt Knowledgebase (UniProtKB) is currently the largest and most comprehensive resource for protein sequence and annotation data. The May 2019 release of the Uniprot Knowledge base (UniprotKB) contains around 158 million protein sequences. For the complete exploitation of this huge knowledge base, protein sequences need to be annotated with functional properties such as Enzyme Commission (EC) numbers and Gene Ontology terms. However, there is only about half a million sequences (UniprotKB/SwissProt) are reviewed and functionally annotated by expert curators using information extracted from the published literature and computational analyses.
Introduction 1. Protein Function Annotation Proteins are important components of all biological systems. Understanding protein function is one of the keys to understanding life at the molecular level, and is central to understanding disease processes and guiding drug discovery efforts  (Bakheet and Doig, 2009). In this age of rapid and affordable amino-acid sequencing technologies, the number of protein sequences accumulating in public databases is rising at an increasing rate  (Berger et al. , 2016). In order to enrich and exploit this huge quantity of data, these sequences should be annotated with functional properties. The UniProt knowledge base (UniProtKB) (The UniProt Consortium, 2015) is currently the largest public sequence database. It consists of two components: (i) the UniProtKB/Swiss-Prot database which a https://orcid.
org/0000-0002-9716-4989 contains protein sequences with reliable information that has been reviewed by expert bio-curators, and (ii) the UniProtKB/TrEMBL database that stores unannotated sequences. Thus, for all proteins in UniProtKB, we have the primary amino acid sequences as well as some further information such as InterPro domain definitions which may have been identified from families of similar sequences or 3D protein structures. Enzymes are usually labelled following the Enzyme Commission (EC) system (Cornish-Bowden, 2014), the widely used numerical enzyme classification scheme. The EC System assigns each enzyme a four digits number. The challenge, therefore, is to annotate the un-annotated protein sequences automatically with appropriate EC number. The EC classification system has a hierarchical structure.
PPS VFS Non-overlapping K-mers have been used for learning word embedding tasks and have been shown to have better prediction accuracy when applied to family classification task  (Asgari and Mofrad, 2015). Overlapping K-mers are widely used in homology based sequence search in large databases of protein sequences like  (Altschul et al. , 1997). This paper presents a novel way to tokenize the protein sequences for the purpose of functional annotation. Instead of k-mers, we use domain and family information of protein in order to learn protein domain embeddings. The rationale behind using domain information is that the domains are the meaningful units of protein sequence conserved across similar sequences. Domains may be considered as natural building blocks of proteins. Due to evolution, protein domains may have gone through changes such as duplication, fusion, recombination to produce proteins with distinct structures and functions  (Kummerfeld and Teichmann, 2009).
On the other hand, kmer words do not carry any biologically significant meaning by themselves. Thus, tokenizing a protein sequence into sentence of domains is more informative than sentence of k-mer words. The experimental results presented in experimental section verify the higher performance of domain embeddings. Two steps are necessary to prepare a training corpus using domain information: (1) Firstly, for each of the sequences, identify domain signatures using InterProScan  (Jones et al. , 2014; Quevillon et al. , 2005; Mitchell et al. , 2018) , which is a sequence analysis software that integrates different protein signature recognition methods into one resource. InterProScan provides domain signatures along with their location of appearance in the sequence. (2) Secondly, InterProScan output is processed to collect the domain signatures and sorting them according to their location of appearance.
Dataset and Training To evaluate the method, we have used a well defined dataset of enzyme and non-enzyme proteins curated from UniprotKB (The UniProt Consortium, 2015). This dataset is called "NEW" and was published by  (Li et al. , 2018)  and was constructed as follows: 1. The SWISS-PROT (released on September 7, 2016) database was separated into enzymes and non-enzymes based on their annotation. 2. To guarantee uniqueness and correctness, enzyme sequences with more than one set of EC numbers or incomplete EC number annotation were excluded. 3. To avoid fragment data, enzyme sequences annotated with fragment or with less than 50 amino acids were excluded. Enzyme sequences with more than 5000 amino acids were also excluded. 4. Redundancy bias is removed using CD-HIT  (Fu et al. , 2012)  with 40% similarity threshold to sift the raw dataset, resulting in 22,168 low-homology enzyme sequences.
5. To construct the non-enzyme part, 22,168 nonenzyme protein sequences were randomly collected from the SWISS-PROT (released on September 7, 2016) non-enzyme part, which were also subject to the above (iiiv) steps. Thus the original dataset contains 22,168 enzymes and an equal number of non-enzymes. To build the corpus for learning the embeddings, each sequence has undergone three different preprocessing treatment for 3 different types of sequence tokenization described in the section 1. 3. For overlapping k-mer, each sequence is split into overlapping K-mer words. We have chosen 3-mer for our experiment meaning a window of size 3 is moved over the sequence to split it into overlapping 3-mers. For non-overlapping k-mer, each sequence was transformed into 3 sequences of non-overlapping 3mers.
And same label is associated with all of the 3 sequences as they are generated from the same sequence with single EC label. In the last case, we used domain and family signatures of proteins as words to build the training corpus. Each sequence was transformed into a list of domains using InterProScan  (Jones et al. , 2014)  software of Version 5. 35-74. 0. We built 3 large corpus using the 3-mers (overlapping and nonoverlapping) and domains and each sentence is associated with EC labels to train the supervised classification model. For non-enzyme, we have labelled them with " label NANZ". For the implementation purpose, we used fast-Text  (Joulin et al. , 2016)  which is library for learning of embeddings and text classification developed by facebook's AI research.
Evaluation To evaluate our method, we used 10-fold crossvalidation on NEW Dataset. We split the text corpus into 10 parts, and considered one of them as a testing set with the remaining 9 parts composedly being used as the training set. The cross validation results show promising performance of the domain embedding based Enzyme classification. For each query sequence, we picked the top ranked annotation only. To validate the performance, we computed the accuracy, macro-precision, macro-recall, and macro-F1measure at different levels of EC number. The following formulae (as used in  (Li et al. , 2018) ) were used to compute the evaluation metrics: accuraccy(y, y ) = 1 N N-1 ∑ i=0 1(y i = y i ), Here, y and y are the list of ground truths and predicted annotations. The accuracy is computed for each level of EC annotation.
For each query sequence, we pick the top-most annotation. For evaluation purposes, we split the 4-digit EC annotation into its constituent parts. Then, for level-1 we consider first digit, for level-2 we take first 2 digits, for level-3 we take first 3-digits and finally for level-4 we take all four digits together. In our experiments, we have trained the model using 0. 9 as learning rate, 150 hidden units, window size of 5 and trained for 200 epochs. We have used Hierarchical Softmax as loss function and word n-grams of 2. The cross validation result is shown in the table  1. Table -1 shows the accuracy, precision, recall and F1 measure of the cross-validation performed over the NEW dataset. As EC numbers consist of four digits, we present the evaluation metrics for four levels (level-1, 2, 3, 4).
Conclusion In this paper, we propose a novel protein function annotation approach using domain embedding based sequence classification instead of k-mer based word embedding. To show the superior performance of the proposed method, we used 10-fold cross-validation on benchmark dataset. We measured the annotation performance using accuracy and we also report the macro precision, macro-recall, and macro-F1 measure to reduce the effect of class imbalance in the test dataset. According to all the evaluation metrics we considered, the proposed approach show better performance. One of the strengths of the proposed method lies in it's simplicity. The method learns domain embeddings using a single layer neural network. Due to the use of shallow neural network, the training is faster than other multi-layer deep networks.
We have used hierarchical softmax loss function to make training even faster. Unlike other hierarchical classification models like ECPred  (Dalkiran et al. , 2018)  and DEEPre  (Li et al. , 2018) , the proposed method learns single model instead of learning many models each for every class. The method is scalable for larger dataset using CUDA based GPU units. Although the proposed method performs well, there is still scope of improvement specially for level-3 and level-4 predictions. As a future plan, we envision to improve the method for more precise predictions and also to apply the similar approach for protein function annotation using Gene Ontology Terms.