dataesr/scientific-paragraphs-categorization huggingface dataset:
Seismic reflectivity At Site U1480, we made additional ties between the cores and the seismic reflection data by comparing the reflectivity with the acoustic impedance (product of P-wave velocity and density) using the best available values for P-wave velocity and density to determine the impedance. Seismic velocity data were selected from PWL and PWC P-wave velocity, using a 5 m median filter to exclude outliers and extract trends on a scale similar to the seismic reflection IODP Proceedings 55 Volume 362 wavelengths. Bulk density values from both GRA density data and from MAD core sample measurements were then used to make correlations between intervals in the core that would produce strong reflections in the seismic profiles, giving additional time-depth tie points. At Site U1481, the cored section of the hole started at 1150 mbsf, so there was no fix at the seafloor.
-State prediction: The predicted state estimate is expressed as: X * t = f ( X t-1 , u t ) -Covariance prediction: The predicted covariance estimate is given by: P * t = A. P t-1. A T + Q -Observation prediction: This step handles prediction's measurement or innovation. Y * t = h(X * t ) -Observation: Measurements and data association are realized. Thus, estimated positions and noise are obtained. -Correction of the predicted state: In this step, we obtain both updated state and covariance estimates. X t = X * t + K(Y -Y * t ) P t = P * t -KHP * t where K is the Kalman gain: K = P * t H T (H. P * t. H T + P Y ) -1.
SLAM Application on a Mobile Robot 4. 1 Presentation of the Robotic System Experimental tests are realized on Wifibot, a well designed robot offering to the user a platform to develop and manipulate different applications in an easy way. The Wifi board ensures a wireless connection of the system with the configured access point which is provided freely. It uses the Hokuyo UT M -30LX laser sensor of the robot which covers 270 • and allows to detect all objects from a highly long distance. Control and measure acquisitions are easier and more reliable using the laser telemeter sensor. By doing 40 scans/second, it can achieve an accuracy of 0. 25 •. SLAM algorithm is applied on Wifibot through the Core SLAM unit. This unit has been developed by pioneered researchers in robotic applications. It uses the laser telemeter's measures and odometric data in order to built the navigation map.
Conclusion In this paper, we have come to explicit a SLAM algorithm based on the Extended Kalman Filter. We have simulated the EKF SLAM on a small scale map. Then, we have introduced an experimental method to explore a Core Slam unit package integrated in a mobile robot. The unit in no longer a black box and its use becomes easier. Obtained large scale map was subject of evaluating performances of SLAM algorithm. Results show that the proposed method is very useful in tracking a defined trajectory although the difference between real and estimated data indicates inaccuracy of the algorithm. For consistent behaviour, the chi-square distribution is being used to test the algorithm performance. The average Normalized Estimation Error Squared besides heading curves show disturbances in addition of several spikes.
The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j. het. io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1. 0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.
Furthermore, integration of multiple channels of evidence, particularly diverse types of data, can overcome the limitations and weak performance inherent to data of a single domain  (Hodos et al. , 2016). Modern computational approaches offer a convenient platform to tie these developments together as the reduced cost and increased velocity of in silico experimentation massively lowers the barriers to entry and price of failure  (Hurle et al. , 2013; Liu et al. , 2013). Hetnets (short for heterogeneous networks) are networks with multiple types of nodes and relationships. They offer an intuitive, versatile, and powerful structure for data integration by aggregating graphs for each relationship type onto common nodes. In this study, we developed a hetnet (Hetionet v1. 0) by integrating knowledge and experimental findings from decades of biomedical research spanning millions of publications.
We adapted an algorithm originally developed for social eLife digest Of all the data in the world today, 90% was created in the last two years. However, taking advantage of this data in order to advance our knowledge is restricted by how quickly we can access it and analyze it in a proper context. In biomedical research, data is largely fragmented and stored in databases that typically do not "talk" to each other, thus hampering progress. One particular problem in medicine today is that the process of making a new therapeutic drug from scratch is incredibly expensive and inefficient, making it a risky business. Given the low success rate in drug discovery, there is an economic incentive in trying to repurpose an existing drug that has already been shown to be safe and effective towards a new disease or condition.
Himmelstein et al. used a computational approach to analyze 50,000 data points -including drugs, diseases, genes and symptoms -from 19 different public databases. This approach made it possible to create more than two million relationships among the data points, which could be used to develop models that predict which drugs currently in use by doctors might be best suited to treat any of 136 common diseases. For example, Himmelstein et al. identified specific drugs currently used to treat depression and alcoholism that could be repurposed to treat smoking addition and epilepsy. These findings provide a new and powerful way to study drug repurposing. While this work was exclusively performed with public data, an expanded and potentially stronger set of predictions could be obtained if data owned by pharmaceutical companies were incorporated.
Our ability to create such an integrative model of drug efficacy relies on the hetnet data structure to unite diverse information. On Hetionet v1. 0, our algorithm learns which types of compound-disease paths discriminate treatments from nontreatments in order to predict the probability that a compound treats a disease. We refer to this study as Project Rephetio (pronounced as rep-het-ee-oh). Both Rephetio and Hetionet are portmanteaus combining the words repurpose, heterogeneous, and network with the URL het. io.
Hetionet v1. 0 We obtained and integrated data from 29 publicly available resources to create Hetionet v1. 0 (Figure  1 ). The hetnet contains 47,031 nodes of 11 types (Table  1 ) and 2,250,197 relationships of 24 types (Table  2 ). The nodes consist of 1552 small molecule compounds and 137 complex diseases, as well as genes, anatomies, pathways, biological processes, molecular functions, cellular components, perturbations, pharmacologic classes, drug side effects, and disease symptoms. The edges represent relationships between these nodes and encompass the collective knowledge produced by millions of studies over the last half century. For example, Compound-binds-Gene edges represent when a compound binds to a protein encoded by a gene. This information has been extracted from the literature by human curators and compiled into databases such as DrugBank, ChEMBL, DrugCentral, and BindingDB.
We combined these databases to create 11,571 binding edges between 1389 compounds and 1689 genes. These edges were compiled from 10,646 distinct publications, which Hetionet binding edges reference as an attribute. Binding edges represent a comprehensive catalog constructed from low-throughput experimentation. However, we also integrated findings from high-throughput technologies -many of which have only recently become available. For example, we generated consensus transcriptional signatures for compounds in LINCS L1000 and diseases in STARGEO. While Hetionet v1. 0 is ideally suited for drug repurposing, the network has broader biological applicability. For example, we have prototyped queries for (a) identifying drugs that target a specific pathway, (b) identifying biological processes involved in a specific disease, (c) identifying the drug targets responsible for causing a specific side effect, and (d) identifying anatomies with transcriptional relevance for a specific disease  (Himmelstein, 2016j).
Each of these queries was simple to write and took less than a second to run on our publicly available Hetionet Browser. Although it is possible that existing services provide much of the aforementioned functionality, they offer less versatility. Hetionet differentiates itself in its ability to flexibly query across multiple domains of information. As a proof of concept, we enhanced the biological process query (b), which identified processes that were enriched for disease-associated genes, using multiple sclerosis (MS) as an example disease. The verbose Cypher code for this query is shown below:  The query above identifies genes that interact with MS GWAS-genes. However, interacting genes are discarded unless they are upregulated in an MS-related anatomy (i. e. anatomical structure, e. g.
Hetionet v1. 0 is accessible via a Neo4j Browser at https://neo4j. het. io. This public Neo4j instance provides users an installation-free method to query and visualize the network. The Browser contains a tutorial guide as well as guides with the details of each Project Rephetio prediction. Hetionet v1. 0 is also available for download in JSON, Neo4j, and TSV formats  (Himmelstein, 2017a). The JSON and Neo4j database formats include node and edge properties -such as URLs, source and license information, and confidence scores -and are thus recommended.
Discussion We created Hetionet v1. 0 by integrating 29 resources into a single data structure -the hetnet. Consisting of 11 types of nodes and 24 types of relationships, Hetionet v1. 0 brings more types of information together than previous leading-studies in biological data integration  (Gligorijevic´and Prz ˇulj, 2015). Moreover, we strove to create a reusable, extensible, and property-rich network. While all the resources we include are publicly available, their integration was a time-intensive undertaking and required careful consideration of legal barriers to data reuse. Hetionet allows researchers to begin answering integrative questions without having to first spend months processing data. Our public Neo4j instance allows users to immediately interact with Hetionet. Through the Cypher language, users can perform highly specialized graph queries with only a few lines of code.
Queries can be executed in the web browser or programmatically from a language with a Neo4j driver. For users that are unfamiliar with Cypher, we include several example queries in a Browser guide. In contrast to traditional REST APIs, our public Neo4j instance provides users with maximal flexibility to construct custom queries by exposing the underlying database. As data has grown more plentiful and diverse, so has the applicability of hetnets. Unfortunately, network science has been naturally fragmented by discipline resulting in relatively slow progress in integrating heterogeneous data. A 2014 analysis identified 78 studies using multilayer networks -a superset of hetnets (heterogeneous information networks) with the potential for additional dimensions, such as time. However, the studies relied on 26 different terms, 9 of which had multiple definitions  (Kivela et al.
Materials and methods Hetionet was built entirely from publicly available resources with the goal of integrating a broad diversity of information types of medical relevance, ranging in scale from molecular to organismal. Practical considerations such as data availability, licensing, reusability, documentation, throughput, and standardization informed our choice of resources. We abided by a simple litmus test for determining how to encode information in a hetnet: nodes represent nouns, relationships represent verbs  (Chen, 1997; Himmelstein et al. , 2016c). Our method for relationship prediction creates a strong incentive to avoid redundancy, which increases the computational burden without improving performance. In a previous study to predict disease-gene associations using a hetnet of pathophysiology  (Himmelstein and Baranzini, 2015a) , we found that different types of gene sets contributed highly redundant information.
Nodes Nodes encode entities. We extracted nodes from standard terminologies, which provide curated vocabularies to enable data integration and prevent concept duplication. The ease of mapping external vocabularies, adoption, and comprehensiveness were primary selection criteria. Hetionet v1. 0 includes nodes from five ontologies -which provide hierarchy of entities for a specific domainselected for their conformity to current best practices  (Malone et al. , 2016). We selected 137 terms from the Disease Ontology  (Schriml et al. , 2012; Kibbe et al. , 2015 ) (which we refer to as DO Slim  (Himmelstein and Li, 2015d; Himmelstein, 2016g )) as our disease set. Our goal was to identify complex diseases that are distinct and specific enough to be clinically relevant yet general enough to be well annotated.
, 2012). We selected a subset of 402 Uberon terms by excluding terms known not to exist in humans and terms that were overly broad or arcane  (Malladi et al. , 2015; Himmelstein, 2016m). Pathways were extracted by combining human pathways from WikiPathways  (Kutmon et al. , 2016; Pico et al. , 2008) , Reactome  (Fabregat et al. , 2016) , and the Pathway Interaction Database  (Schaefer et al. , 2009). The latter two resources were retrieved from Pathway Commons (RRID: SCR_002103)  (Cerami et al. , 2011) , which compiles pathways from several providers. Duplicate pathways and pathways without multiple participating genes were removed  (Pico and Himmelstein, 2015; Himmelstein and Pico, 2016a). Biological processes, cellular components, and molecular functions were extracted from the Gene Ontology  (Ashburner et al.
We compiled these interactions from the Human Interactome Database  (Rual et al. , 2005; Venkatesan et al. , 2009; Yu et al. , 2011; Rolland et al. , 2014) , the Incomplete Interactome  (Menche et al. , 2015) , and our previous study  (Himmelstein and Baranzini, 2015a). Gene-participates-Biological Process, Gene-participates-Cellular Component, and Gene-participates-Molecular Function edges are from Gene Ontology annotations  (Huntley et al. , 2015). As described in Intermediate resources, annotations were propagated  (Himmelstein et al. , 2015g; Himmelstein et al. , 2015f). Gene-participates-Pathway edges were included from the human pathway resources described in the Nodes section  (Pico and Himmelstein, 2015; Himmelstein and Pico, 2016a).
, 2016k). First, we discarded non-gold (nonreplicating or indistinct) signatures. Then, we meta-analyzed z-scores using Stouffer's method. Each signature was weighted by its average Spearman's correlation to other signatures, with a 0. 05 minimum, to de-emphasize discordant signatures. Our signatures include the 978 measured genes and the 6489 imputed genes from the 'best inferred gene subset'. To identify significantly dysregulated genes, we selected genes using a Bonferroni cutoff of p=0. 05 and limited the number of imputed genes to 1000. The consensus signatures for genetic perturbations allowed us to assess various characteristics of the L1000 dataset. First, we looked at whether genetic interference dysregulated its target gene in the expected direction  (Himmelstein, 2016c). Looking at measured z-scores for target genes, we found that the knockdown perturbations were highly reliable, while the overexpression perturbations were only moderately reliable with 36% of overexpression perturbations downregulating their target.
Graph databases and Neo4j Traditional relational databases -such as SQLite, MySQL, and PostgreSQL -excel at storing highly structured data in tables. Connectivity between tables is accomplished using foreign-key references between columns. However, for many biomedical applications the connectivity between entities is of foremost importance. Furthermore, enforcing a rigid structure of what attributes an entity may possess is less important and often unnecessarily prohibitive. Graph databases focus instead on capturing connectivity (relationships) between entities (nodes). Accordingly, graph databases such as Neo4j offer greater ease when modeling biomedical relationships and superior performance when traversing many levels of connectivity  (Yoon et al. , 2017; Jaiswal, 2013). Until recently, graph database adoption in bioinformatics was limited  (Have and Jensen, 2013).
However lately, the demand to model and capture biological connectivity at scale has led to increasing adoption  (Lysenko et al. , 2016; Balaur et al. , 2016; Summer et al. , 2016; Mungall et al. , 2017). We used the Neo4j graph database for storing and operating on Hetionet and noticed major benefits from tapping into this large open source ecosystem  (Himmelstein, 2015m). Persistent storage with immediate access and the Cypher query language -a sort of SQL for hetnets -were two of the biggest benefits. To facilitate our migration to Neo4j, we updated hetio -our existing Python package for hetnets  (Himmelstein, 2016g)  -to export networks into Neo4j and DWPC queries to Cypher. In addition, we created an interactive GraphGist for Project Rephetio, which introduces our approach and showcases its Cypher queries.
Indication sets We evaluated our predictions on four sets of indications as shown in Figure  3. Disease Modifying -the 755 disease-modifying treatments in PharmacotherapyDB v1. 0. These indications are included in the hetnet as treats edges and used to train the logistic regression model. Due to edge dropout contamination and self-testing  (Himmelstein, 2016h; Lizee and Himmelstein, 2016b) , overfitting could potentially inflate performance on this set. Therefore, for the three remaining indication sets, we removed any observations that were positives in this set. DrugCentral -We discovered the DrugCentral database after completing our physician curation for PharmacotherapyDB. This database contained 210 additional indications  (Himmelstein et al. , 2016d). While we didn't curate these indications, we observed a high proportion of disease-modifying therapy.
, 2016t; Waldrop, 2015) ; and improving our documentation by maintaining a publication-grade digital lab notebook  (Giles, 2012). Thinklab began winding down operations in July 2017 and has switched to a static state. While users will no longer be able to add comments, the corpus of content remains browsable at https:// think-lab. github. io and available in machine-readable formats at dhimmel/thinklytics. The preprint for this study is available at doi. org/bs4f  (Himmelstein et al. , 2016u). The manuscript was written in markdown, originally on Thinklab at doi. org/bszr  (Himmelstein et al. , 2016v). In August 2017, we switched to using the Manubot system to generate the manuscript. With Manubot, a GitHub repository (dhimmel/rephetio-manuscript) tracks the manuscript's source code, while continuous integration automatically rebuilds the manuscript upon changes.
Software and data availability All software and datasets from Project Rephetio are publicly available on GitHub, Zenodo, or Figshare  (Himmelstein et al. , 2017b). Additional documentation for these materials is available in the corresponding Thinklab discussions. For reader convenience, software, datasets, and Thinklab discussions have been cited throughout the manuscript as relevant. Copies of the most relevant Github repositories are archived at: https://github. com/elifesciences-publications/hetionet; https://github. com/elifesciences-publications/integrate; https://github. com/elifesciences-publications/ learn; https://github. com/elifesciences-publications/hetio and https://github. com/elifesciences-publications/rephetio-manuscript.
Acknowledgements We are immensely grateful to our Thinklab contributors who joined us in our experiment of radically open science. The following non-team members provided contributions that received five or more Thinklab points:  Lars Juhl Jensen ,  Frederic Bastian ,  Alexander Pico ,  Casey Greene ,  Benjamin Good ,  Craig Knox ,  Mike Gilson ,  Chris Mungall ,  Katie Fortney ,  Venkat Malladi ,  Tudor Oprea ,  MacKenzie Smith ,  Caty Chung ,  Allison McCoy ,  Alexey Strokach ,  Ritu Khare ,  Greg Way ,  Marina Sirota ,  Raghavendran Partha ,  Oleg Ursu ,  Jesse Spaulding ,  Gaya Nadarajan ,  Alex Ratner ,  Scooter Morris ,  Alessandro Didonna ,  Alex Pankov ,  Tong Shu Li , and  Janet Pin ˜ero. Additionally , the founder of Thinklab, Jesse Spaulding, supported community contributions and developed the platform with Project Rephetio's needs in mind.
We also appreciate DigitalOcean's sponsorship the  Hetionet Browser  to cover its hosting costs. Finally, we would like to thank  Neo Technology , whose staff provided excellent technical support. This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship  under Grant Number  1144247  to DSH. SEB is supported by  NINDS/NIH  grant number  5R01NS088155  and the  Heidrich Family and Friends Foundation. DH is supported by the the  National Cancer Institute of the National Institutes of Health  under Award Number  UH2CA203792  and the  National Library of Medicine  under Award Number  1U01LM012675. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH.
Long-term place recognition in outdoor environments remains a challenge due to high appearance changes in the environment. The problem becomes even more difficult when the matching between two scenes has to be made with information coming from different visual sources, particularly with different spectral ranges. For instance, an infrared camera is helpful for night vision in combination with a visible camera. In this paper, we emphasize our work on testing usual feature point extractors under both constraints: repeatability across spectral ranges and long-term appearance. We develop a new feature extraction method dedicated to improve the repeatability across spectral ranges. We conduct an evaluation of feature robustness on long-term datasets coming from different imaging sources (optics, sensors size and spectral ranges) with a Bag-of-Words approach.
We complement this new descriptor definition with experimental results on local features applied to the long-term localization problem with a Bag-of-Words approach and establish a benchmark including our algorithm and the most popular ones. We show that our proposal outperforms the usual methods on datasets with important constraints generated by images acquired in distinct spectral ranges (visible-NIR, visible-SWIR and visible-LWIR). We itemize our contributions as follows: firstly (Section 2), we lead an analysis of the different state-of-art approaches facing multimodal datasets in the context of visual place recognition. In a second phase (Section 3), we give details on the feature extractor we design and the reasons which have led to such choices. In a third part (Section 4), we confront our proposal to several datasets used in the visual place recognition community and an additional one we acquired to push further the constraints to the limits.