dataesr/scientific-paragraphs-categorization huggingface dataset:
Defining a Virtual City Being applied as a case study to the french city of Grenoble (160,000 inhabitants), the model requires us to create a virtual city as much empirically grounded as possible, taking into account multi-sources data, being whether spatial (urban ressources), temporal (opening hours of the urban resources) or related to the inhabitants characteristics and their daily patterns of activities. To do so, we deal with several databases and surveys and call on various statistical methods.
Constitution of the Spatio-Temporal Database of Opening Hours of Grenoble Urban Resources The model developed relies on a spatio-temporal database detailing the location as well as the opening and closing hours of urban resources, defined here as the set of services, shops, businesses and public institutions to which individuals move in order to achieve their daily activities. Facing the absence of such data in Grenoble (and more generally in most French cities) we have therefore developed an appropriate methodology to build the needed database. Concerning public facilities (town hall, schools, police, etc. ), the data were collected exhaustively, due to the small number of resources involved (about 900). Data were collected directly via Internet and telephone contacts. The approach is somehow different for shops, services and businesses.
We decided to complete a database bought from the Chambre de Commerces et d'Industrie (CCI for Chamber of Commerce and Industry). This database describes all shops, services and businesses in terms of its activity category, number of employees, location, etc. , of a given area. To complete this existing database, the following diagram illustrates the overall methodology we developed (Figure  3 ). First, the resources from the CCI database were ordered by their "APE", or "main activity code", which is a classification of the building main activity. All the resources were then geolocated using MapInfo and ArcGIS softwares, plus Google Maps database. In total, 97% of resources were located with sufficient accuracy. To assign the opening and closing hours for shops, services and businesses, a telephone survey was carried out.
Given the large number of resources in the 26 municipalities of the study area, a sample (14,451) was selected. The proportional stratified random sampling method was applied. This method consists in dividing the study population into subpopulations and selecting independent samples within each stratum using the same sampling rate. The sampling criteria that were taken into account are the activity type and the density of resources for shops and services, as well as the activity type and the number of employees for companies. In total, 12% of shops and services and 9% of companies were surveyed. Once the opening hours were collected for the samples, we generalized them to the remaining population taking into account the type of activity and the density of resources within the area. For that purpose, a Python script was developed.
Survey Data as an Input In order to create a model based on observed daily mobility, one of the most thorough dataset available in France is the "Enquête Ménages-Déplacement", or "EMD" (household travel survey). This standardized survey is focused on the daily mobility in large urban districts. The data production draws upon questionnaires given to randomly-selected households inside specific representative zones of the urban area, between the end of November 2009 and beginning of April 2010, excluding holidays and weekends. The collected data are focused on the day preceding the survey for each individual, and cover a 24 h period, in which every journey must be as detailed as possible in space and time. The 2010 EMD survey used in this work collected 7600 households from Grenoble's urban area in 97 districts, which means around 16,000 people aged 5 years and over.
Generation of Agent's Schedule Once created, each agent picks-up a schedule in a predefined library of schedules (Table  5 ). The predefined library of schedule, is derived from the full sets of combinations revealed by the EMD survey. A Python program, has been developed in order to create a set of around 5000 possible combinations of activities, according to the EMD survey. At first, the logical combinations of all activities are generated, under the following list of constraints: existence of minimum (1) and a maximum (3) number of activities in a day; necessity for schedules to begin and end at "Home"; absence of redundancy for "Home". These conditions are based on the analysis of the survey, revealing that the part of the population concerned by a schedule with more than three daily activities is not significant.
Acknowledgments: The  MIRO  project was funded successively by  PREDIT3  (contract MT  03MT66 , scientific coordinator Christophe Lang), by the  french National Agency for Research  (contract  ANR-08-VD-00 , scientific coordinator  Arnaud Banos ) and the  French Ministry of Ecology, Sustainable Development and Energy  (contract  13-MT-GO6-8-CVS-038 2013 , scientific coordinator  Arnaud Banos ).
RESULTS AND DISCUSSION In order to investigate NAM functionality in mGlu5 in silico, the missing second intracellular and extracellular loops (ICL2 and ECL2) of the mGlu5 crystal structure (PDB id 4OO9) were constructed with MODELLER  [32]  and unnatural amino-acids in the TM domain converted back to wild-type with CHIMERA  [33]  (see Methods). As a control for validating a reliable NAM docking protocol for wt mGlu5, we redocked the co-crystallized NAM mavoglurant back into the empty allosteric binding pocket of the TM domain using Autodock-4. 2  [34] , seeking to reproduce the crystal binding pose. Fig. 2  shows the top-ranked docking solution of mavoglurant in wt mGlu5, with an RMSD of 0. 2 Å compared to the crystal state. This best-ranked solution reestablishes all protein-ligand H-bonds and important interactions observed in the original crystal structure.
Other differences compared to mavoglurant include a lack of protein-ligand Hbonds with respect to N747 on TM5 and S805 on TM7. These "missing" interactions are a result of MPEP being shorter in length and not containing some of the chemical groups possessed by mavoglurant. The rest of the receptor, including the co-crystallized water molecule between TM3, TM6 and TM7 (identified by Marshall et al. [24] ) remains unchanged with respect to the original crystal structure (except mutated residues reverted back to wt). In order to validate the predicted binding-mode of MPEP, we pursued two different exploratory lines: (i) experimentally   2 ). The best-ranked docking solution of mavoglurant (in blue) in the complete wt structure of mGlu5 (in beige). All protein-ligand Hbonds originally identified in the crystal structure (PDB id: 4OO9) were reformed during docking (orange lines, involving residues N747, S805, S809).
Experimental HEK293 cells were transiently transfected with mGlu5 receptors by electroporation. Receptors were cotransfected with EAAC1, a glutamate transporter, to avoid the influence of extracellular glutamate. The receptor activity was monitored through measurements of inositol monophosphate (IP1) production. IP1 production was determined using the IP-One HTRF kit (CisBio Bioassays) according to the manufacturer's recommendations  [48]. All points were performed in triplicate. Mutant receptors were obtained using the Quick-Change strategy (Stratagene, La Jolla, CA, USA) and all mutations were verified by sequencing. Experimental data were fitted using the Hill equation with Prism software (GraphPad, La Jolla, CA, USA). Quisqualate and MPEP were purchased from Tocris Bioscience (Bristol, UK).
Completion of wt TM mGlu5 Structure The TM mGlu5-lysozyme fusion crystal structure (PDB id 4OO9) was completed by first cleaving lysozyme and removing solvent and fatty acid molecules from the structure, then modelling missing second intracellular and extracellular loops (ICL2 and ECL2) with MODELLER  [32]  and converting unnatural amino-acids and thermostabilising mutations in the TM domain back to wild-type with CHIMERA  [33]  (C634, C691, E579, N667, I669, G675, T742, S753). This was done by selecting the most appropriate bestfit wt rotamer with highest possible statistical probability. Co-crystallized water molecules were maintained in the structure and mavoglurant ligand was removed.
Docking of NAMs Coordinates of mGlu5 were extracted from OPM database  [49] , PDB id: 4OO9  [24] , reorientated in an implicit membrane. Cis and trans optimized conformations of the azo and amide groups for alloswitch-1 were respectively obtained by Density Functional Theory (DFT) at the B3LYP/6-31+G(d) level using Gaussian  [42]. Coordinates for MPEP and mavoglurant were generated with Maestro  [50]. Autodock 4. 2 was used with "-B amide" flag to give flexibility to ligand amide groups and flag "-U nphs_lps_nonstdres" to maintain co-crystallized waters in the receptor structure. Grid points were extended to cover total allosteric pocket volume. For docking cis-azo alloswitch-1 (photoswitched state), full ligand flexibility was ensured except the azo dihedral angle, which was maintained at +12° as indicated by DFT calculations.
Conformational Sampling of mGlu5 Through structural inspection of the mGlu5 crystal structure (PDB id: 4OO9), residue F788 on TM6 was identified as influential in determining the conformational shape and volume of the allosteric pocket of mGlu5. Therefore an alternative conformation of mGlu5 for docking alloswitch-1 was identified by adjusting the rotameric position of F788 from inward to outward pointing in the mGlu5 crystal structure using CHIMERA  [33]  (selection of second most probable rotamer). This structure was then relaxed with ROSETTA  [52]  (minus ligand) in an implicit membrane, generating up to 1000 possible conformations, clustered at 2 Å, and the highest scoring conformation in the largest cluster selected as the final structure. The co-crystallized water molecule at the bottom of the allosteric pocket, as well as F788 sidechain, were positionally constrained during this process.
Database Searching Crystallized non-cyclic cis-amide containing-ligands (amide dihedral angle: 0°±20°) were identified in the CSD  [45]  by using the Mogul tool  [57]  v1. 7 and non-cyclic cisamide ligands were identified in the PDB  [46]  by geometric matching (with an inhouse script) against representative nonredundant ligands in PDBeChem  [58]  using an idealized cisamide search fragment (i. e. with 0° amide dihedral angle).
ACKNOWLEDGEMENTS This study was supported in part by  Ministerio de Economía y Competitividad  ( SAF2010-19257 ,  ERA-NET NEURON PCIN-2013-018-C03-02 ) and  Fundació La Marató de TV3  (Refs. 110230, 110231 and 110232). We acknowledge the support of  Generalitat de Catalunya  ( 2009SGR-1072 ) and CSIC. Computations were performed at the  Center for Scientific and Academic Services of Catalonia (CESCA). The authors participate in the European COST Action CM1207 (GLISTEN: GPCR-Ligand Interactions, Structures, and  Transmembrane Signalling : a  European Research Network ).
Biology has become a data-intensive research field. Coping with the flood of data from the new genome sequencing technologies is a major area of research. The exponential increase in the size of the datasets produced by "next-generation sequencing" (NGS) poses unique computational challenges. In this context, motif discovery tools are widely used to identify important patterns in the sequences produced. Biological sequence motifs are defined as short, usually fixed length, sequence patterns that may represent important structural or functional features in nucleic acid and protein sequences such as transcription binding sites, splice junctions, active sites, or interaction interfaces. They can occur in an exact or approximate form within a family or a subfamily of sequences. Motif discovery is therefore an important field in bioinformatics, and numerous methods have been developed for the identification of motifs shared by a set of functionally related sequences.
However, the scalable, flexible nature of nextgeneration sequencing (NGS) technology makes it equally useful for sequencing any species, such as agriculturally important livestock, plants, or disease-related microbes. The exponential increase in the size of the datasets produced by this new generation of instruments clearly poses unique computational challenges. A single run of a NGS machine can produce terabytes of data, and even after image processing, base calling, and assembly, there will be hundreds of gigabytes of uncompressed primary data that must be stored either in flat files or in a database. Efficient treatment of all this data will require new computational approaches in terms of data storage and management, but also new effective algorithms to analyze the data and extract useful knowledge.
The major challenge today is to understand how the genetic information encoded in the genome sequence is translated into the complex processes involved in the organism and the effects of environmental factors on these processes. Bioinformatics plays a crucial role in the systematic interpretation of genome information, associated with data from other highthroughput experimental techniques, such as structural genomics, proteomics, or transcriptomics. A widely used tool in all these stages is the comparison (or alignment) of the new genetic sequences with existing sequences. During genome assembly, short read sequences are often aligned to a reference genome to form longer contigs. Identification of coding regions then involves alignment of known genes to the new genomic sequence. Finally, functional significance is most often assigned to the protein coding regions by searching public databases for similar sequences and by transferring the pertinent information from the known to the unknown protein.
A wide variety of computational algorithms have been applied to the sequence comparison problem in diverse domains, notably in natural language processing. Nevertheless, the analysis of biological sequences involves more than abstract string parsing, for behind the string of bases or amino acids is the whole complexity of molecular and evolutionary biology. One major problem is the identification of important features, such as regulatory sites in the genomes, or functional domains or active sites in proteins, that are conserved within a family of sequences, without prior alignment of the sequences. In this context, motif recognition and discovery tools are widely used. The retrieved motifs are often compiled in databases including DNA regulatory motifs in TRANSFAC  [1] , JASPAR  [2] , or RegulonDB  [3] , and protein motifs in PRINTS  [4] , PROSITE  [5] , or ELM  [6].
Motif recognition in protein sequences The motif recognition problem takes as input a set of known patterns or features that in some way define a class of proteins. The goal is then to search in an unsupervised or supervised way for other instances of the same patterns. As mentioned in the Introduction, the known motifs in biological sequences are generally compiled databases that are publically available over the Internet. For example, the PRINTS database (www. bioinf. manchester. ac. uk/dbbrowser/ PRINTS) contains "protein fingerprints," where a fingerprint is composed of a group of motifs that characterize a given set of protein sequences with the same molecular function. In contrast, the PROSITE (prosite. expasy. org) and ELM (elm. eu. org) databases contain single motifs that correspond to known functionally or structurally important amino acids, such as those involved in an active site or a ligand binding site.
Probabilistic models Although deterministic models provide useful ways to construct human-readable representations of motifs, their main drawback is that they lose some information. For instance, in the IQ motif discussed above, the first position is usually I and both [RK] are most often R. Probabilistic models can be used to overcome such loss of information. The position-specific scoring matrix (PSSM)  [17] , also known as the probability weight matrix (PWM), is undoubtedly one of the most widely used probabilistic models. This model is represented by a matrix where each entry (i,a) is the probability of finding an amino acid a at the ith position in the sequence motif. For example, for a set of motifs: • WSEW • WSRW • CSKW • CSKW • YSKY The corresponding PSSM is shown in Table  1. Although in this example, PSSM containing entries having a value of 0, in general, pseudocounts are applied, especially when using a small dataset, in order to allow the calculation of probabilities for new motifs.
qPMS  [24]  stands for quorum planted motif search. The program searches for motifs in either DNA or protein sequences. It uses the (l, d) motif search algorithm known as the planted motif search. qPMS takes as input a set of sequences and two values, l and d. It returns all sequences M of length l, which appear in at least q% of the sequences. SLiMFinder  [13]  identifies novel short linear motifs (SLiMs) in a set of sequences. SLiMs are microdomains that have important functions in many diverse biological pathways. SLiMmediated functions include posttranslational modification, subcellular localization, and ligand binding. SLiMs are generally less than 10 amino acids long, many of which will be "flexible" in terms of the conserved amino acid. SLiMFinder constructs such motifs by grouping dimers into longer patterns: motifs with fixed amino acid positions are identified and then grouped to include amino acid ambiguity and variable-length wildcards.
Finally, motifs that are overrepresented in a set of unrelated proteins are identified. Dilimot  [25]  proceeds as follows: in the first step, a user provided set of protein sequences is filtered to eliminate repetitive sequences as well as the regions least likely to contain linear motifs. In the second step, overrepresented motifs are identified in the nonfiltered sequences and ranked according to scores that take into account the background probability of the motif, the number of sequences containing the motif, the size of the sequence set, and the degree to which the motif is conserved in other orthologous proteins. MotifHound  [26]  is suitable for the discovery of small and degenerate linear motifs. The method needs two input datasets: a background set of protein sequences and a subset of this background set that represents the query sequences.
Program FIRE-pro  [27]  stands for finding informative regulatory elements in proteins. Its main goal is to discover protein motifs that correlate with the biological behavior of the corresponding proteins. FIRE-pro calculates a mutual information measure between frequent k-mer motifs and a "protein behavior profile" containing experimental data about the function of the proteins. Most of these programs need prior knowledge about either the input sequences or the motif structure. Furthermore, they are generally designed to discover frequent motifs that occur in all or most of the sequences. The subfamily-specific motifs, which differentiate a specific subset of sequences, pose a greater challenge due to the statistical nature of these programs or the default choice of parameters used. Nevertheless, these "rare" motifs are often characteristic of important biological functions or context-specific modifications, including substrate binding sites, protein-protein interactions, or posttranslational modification sites.
Another advantage of AIS is the fact that there are varying types of elements that protect the body from invaders, and there are different lines of defense, such as innate and adaptive immunity. These features can be abstracted to model the diverse types of motifs found in protein molecules (see Section 1). These different mechanisms are organized in multiple layers that act cooperatively to provide high noise tolerance and high overall security. The use of such intelligent algorithmic approaches should improve the whole motif discovery process: from the selection of suitable sets of sequences, via data cleaning and preprocessing, motif identification and evaluation, to the final presentation and visualization of the results. Nevertheless, a number of issues remain to be addressed before such systems can be applied to the very large datasets produced by NGS technologies.
Convolutional neural networks (CNN) are widely used in computer vision, especially in image classification. However, the way in which information and invariance properties are encoded through in deep CNN architectures is still an open question. In this paper, we propose to modify the standard convolutional block of CNN in order to transfer more information layer after layer while keeping some invariance within the network. Our main idea is to exploit both positive and negative high scores obtained in the convolution maps. This behavior is obtained by modifying the traditional activation function step before pooling. We are doubling the maps with specific activations functions, called MaxMin strategy, in order to achieve our pipeline. Extensive experiments on two classical datasets, MNIST and CIFAR-10, show that our deep MaxMin convolutional net outperforms standard CNN.
An appealing feature of deep learning is the ability to learn representations from raw pixels. Today CNN introduced for the first time in  [10]  and popularized by  [11]  are widely used is computer vision and stand as the state of the art in image classification on many popular datasets. Thanks to DGA for funding. The CNN architecture can be described as a succession of convolutional blocks followed by some fully connected layers. Classically, one convolution block successively applies linear filters, non-linear activation functions (like ReLU ), and local pooling operations  [8]. In BoW, the pooling phase enables to manage many invariances but it is also responsible for a loss of spatial information that make the learning difficult. In deep convolutional architectures, activation functions and pooling may be also questioned.
EXPERIMENTS We test our method on two well-known datasets, MNIST and CIFAR-10, and we compare our results with classical CNN. Learning protocol: All the models and the learning framework are implemented in Lua using Torch 7 library in  [24]   1. For the training, we use the same gradient descent as in  [8]  with 0. 9 of momentum and fixed weight decay (10 -3 for MNIST and 10 -4 for CIFAR). We use an equal learning rate for all layers that is being manually reduced when validation error stops decrease. All weights of convolutional filters are initialized from a zero-mean Gaussian distribution with standard deviation 0. 01. The number of training epochs depends on the network but is between 30 and 120 for CIFAR-10 and around 250 for MNIST. Top-1 accuracy on test set is computed to evaluate performances.
MNIST RESULTS MNIST is a 60,000 images dataset representing 9 handwritten digits. Images are 32 × 32 pixels in one channel. See  [25]  for more information. We use the same protocol as  [25] : 50,000 images for training and the 10,000 remaining images for testing. To compare our method, we use a LeNet like network with pooling layers. It is composed of three convolutional layers with ReLU activations all followed by max pooling and a local contrast normalisation layers. The filters are of size 5 × 5 with a stride of 1. There is 64 of them at each convolutional layers. The pooling windows are of size 3 × 3 for a stride of 2. Those layers are followed by one fully connected layers before a softmax. For the MaxMin network setting, we simply include a MaxMin layer before the ReLU activation function.
We keep the same number of filters paying attention to double their size when needed. The baseline network obtains a score of 99. 34% accuracy. With our MaxMin network, we obtain a score of 99. 39% accuracy. Our strategy has 31 errors on the validation set against 36 errors for the baseline CNN. It corresponds to an relative improvement of 13. 9%. Note that the performance scores on this dataset are very high, and any small performance gain is difficult to obtain.