pinecone/core-2020-05-10-deduplication huggingface dataset:
Outdoor flight testing of a pole inspection UAV incorporating high-speed vision

We present a pole inspection system for outdoor environments comprising a high-speed camera on a vertical take-off and landing (VTOL) aerial platform. The pole inspection task requires a vehicle to fly close to a structure while maintaining a fixed stand-off distance from it. Typical GPS errors make GPS-based navigation unsuitable for this task however. When flying outdoors a vehicle is also affected by aerodynamics disturbances such as wind gusts, so the onboard controller must be robust to these disturbances in order to maintain the stand-off distance. Two problems must therefor be addressed: fast and accurate state estimation without GPS, and the design of a robust controller. We resolve these problems by a) performing visual + inertial relative state estimation and b) using a robust line tracker and a nested controller design. Our state estimation exploits high-speed camera images (100Hz) and 70Hz IMU data fused in an Extended Kalman Filter (EKF). We demonstrate results from outdoor experiments for pole-relative hovering, and pole circumnavigation where the operator provides only yaw commands. Lastly, we show results for image-based 3D reconstruction and texture mapping of a pole to demonstrate the usefulness for inspection tasks
SADDLE: A Modular Design Automation Framework for Cluster Supercomputers
  and Data Centres

In this paper we present SADDLE, a modular framework for automated design of
cluster supercomputers and data centres. In contrast with commonly used
approaches that operate on logic gate level (Verilog, VHDL) or board level
(such as EDA tools), SADDLE works at a much higher level of abstraction: its
building blocks are ready-made servers, network switches, power supply systems
and so on. Modular approach provides the potential to include low-level tools
as elements of SADDLE's design workflow, moving towards the goal of electronic
system level (ESL) design automation. Designs produced by SADDLE include
project documentation items such as bills of materials and wiring diagrams,
providing a formal specification of a computer system and streamlining assembly
operations.Comment: 13 pages, 2 figures, 1 table. The work was presented at the
  International Supercomputing Conference (ISC'14) in Leipzig, German
Scalability and parallel execution of OmpSs-OpenCL tasks on heterogeneous CPU-GPU environment

With heterogeneous computing becoming mainstream, researchers and software vendors have been trying to exploit the best of the underlying architectures like GPUs or CPUs to enhance performance. Parallel programming models play a crucial role in achieving this enhancement. One such model is OpenCL, a parallel computing API for cross platform computations targeting heterogeneous architectures. However, OpenCL is a low-level programming language, therefore it can be time consuming to directly develop OpenCL code. To address this shortcoming, OpenCL has been integrated with OmpSs, a task-based programming model to provide abstraction to the user thereby reducing programmer effort. OmpSs-OpenCL programming model deals with a single OpenCL device either a CPU or a GPU. In this paper, we upgrade OmpSs-OpenCL programming model by supporting parallel execution of tasks across multiple CPU-GPU heterogeneous platforms. We discuss the design of the programming model along with its asynchronous runtime system. We   investigated scalability of four OmpSs-OpenCL benchmarks across 4 GPUs gaining speedup of up to 4x. Further, in order to achieve effective utilization of the computing resources, we present static and work-stealing scheduling techniques. We show results of parallel execution of applications using OmpSs-OpenCL model and use heterogeneous workloads to evaluate our scheduling techniques on a heterogeneous CPU-GPU platform.We thankfully acknowledge the support of the European Commission through the TERAFLUX  project  (FP7-249013)  and  the  HiPEAC-2  Network  of  Excellence (FP7/ICT 217068),the support of the Spanish Ministry of Education (TIN-2007-60625, TIN-2012-34557, CSD2007-00050 and FI program) and the Generalitat de Catalunya (2009-SGR-980)Peer ReviewedPostprint (author’s final draft
Model-Based Identification of Anatomical Boundary Conditions in Living Tissues

International audienceIn this paper, we present a novel method dealing with the identification of boundary conditions of a deformable organ, a particularly important step for the creation of patient-specific biomechani-cal models of the anatomy. As an input, the method requires a set of scans acquired in different body positions. Using constraint-based finite element simulation, the method registers the two data sets by solving an optimization problem minimizing the energy of the deformable body while satisfying the constraints located on the surface of the registered organ. Once the equilibrium of the simulation is attained (i.e. the organ registration is computed), the surface forces needed to satisfy the constraints provide a reliable estimation of location, direction and magnitude of boundary conditions applied to the object in the deformed position. The method is evaluated on two abdominal CT scans of a pig acquired in flank and supine positions. We demonstrate that while computing a physically admissible registration of the liver, the resulting constraint forces applied to the surface of the liver strongly correlate with the location of the anatomical boundary conditions (such as contacts with bones and other organs) that are visually identified in the CT images
Comparative Assessment of a Novel Optical Human-Machine Interface for Laparoscopic Telesurgery

International audienceThis paper introduces a novel type of human-machine in-terface for laparoscopic telesurgery that employs an optical sensor. A Raven-II laparascopic robot (Applied Dexterity Inc) was teleoperated us-ing two different human-machine interfaces, namely the Sigma 7 electro-mechanical device (Force Dimension Sarl) and the Leap Motion (Leap Motion Inc) infrared stereoscopic camera. Based on this hardware plat-form, a comparative study of both systems was performed through ob-jective and subjective metrics, which were obtained from a population of 10 subjects. The participants were asked to perform a peg transferring task and to answer a questionnaire. Obtained results allow to confirm that fine tracking of the hand could be performed with the Leap Mo-tion sensor. Such tracking comprises accurate finger motion acquisition to control the robot's laparoscopic instrument jaws. Furthermore, the observed performance of the optical interface proved to be comparable to that of traditional electro-mechanical devices, such as the Sigma 7, during adequate execution of highly-dexterous laparascopic gestures
Task-Oriented Conversational Behavior of Agents for Collaboration in Human-Agent Teamwork

International audienceCoordination is an essential ingredient for human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. This paper proposes a be-havioral architecture C 2 BDI that enhances the knowledge sharing using natural language communication between team members. Collaborative conversation protocols and resource allocation mechanism have been defined that provide proactive behavior to agents for coordination. This architecture has been applied to a real scenario in a collaborative virtual environment for learning. The solution enables users to coordinate with other team members
Influence of participation rates and service level differentiation on community driven predictions

Anticipatory Vehicle Routing based on Intention Propagation (AVRIP) can help reduce drivers travel times and avoid forming congestion. The route guidance system uses information shared by participating drivers to predict future link traversal times, the time it will take a vehicle to traverse a road at a certain time in the future. Both participating and non-participating drivers benefit from these link travel time predictions. Participating drivers will receive the predictions and will adapt their route to avoid any congestion. Non-participating drivers experience less congestion because of these diversions.
The percentage of drivers participating in the AVRIP guidance is an important factor. This participation rate influences the efficiency of the system in two ways: it affects the accuracy of the predictions and it changes the number of drivers influenced by the predictions.
This paper provides a first study on the influence of the participation rate on the efficiency of AVRIP by varying the participation rate while keeping all other parameters constant in a simulated traffic network.status: publishe
Anticipatory coordination of electric vehicle allocation to fast charging infrastructure

The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%.
The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%.status: publishe
From Indexing Data Structures to de Bruijn Graphs

International audienceNew technologies have tremendously increased sequencing throughput com-pared to traditional techniques, thereby complicating DNA assembly. Hence, as-sembly programs resort to de Bruijn graphs (dBG) of k-mers of short reads to compute a set of long contigs, each being a putative segment of the sequenced molecule. Other types of DNA sequence analysis, as well as preprocessing of the reads for assembly, use classical data structures to index all substrings of the reads. It is thus interesting to exhibit algorithms that directly build a dBG of order k from a pre-existing index, and especially a contracted version of the dBG, where non branching paths are condensed into single nodes. Here, we formalise the relation-ship between suffix trees/arrays and dBGs, and exhibit linear time algorithms for constructing the full or contracted dBGs. Finally, we provide hints explaining why this bridge between indexes and dBGs enables to dynamically update the order k of the graph
Reversal Distances for Strings with Few Blocks or Small Alphabets

International audienceWe study the String Reversal Distance problem, an extension of the well-known Sorting by Reversals problem. String Reversal Distance takes two strings S and T as input, and asks for a minimum number of reversals to obtain T from S. We consider four variants: String Reversal Distance, String Prefix Reversal Distance (in which any reversal must include the first letter of the string), and the signed variants of these problems, namely Signed String Reversal Distance and Signed String Prefix Reversal Distance. We study algorithmic properties of these four problems, in connection with two parameters of the input strings: the number of blocks they contain (a block being maximal substring such that all letters in the substring are equal), and the alphabet size Σ. For instance, we show that Signed String Reversal Distance and Signed String Prefix Reversal Distance are NP-hard even if the input strings have only one letter
On Combinatorial Generation of Prefix Normal Words

A prefix normal word is a binary word with the property that no substring has
more 1s than the prefix of the same length. This class of words is important in
the context of binary jumbled pattern matching. In this paper we present an
efficient algorithm for exhaustively listing the prefix normal words with a
fixed length. The algorithm is based on the fact that the language of prefix
normal words is a bubble language, a class of binary languages with the
property that, for any word w in the language, exchanging the first occurrence
of 01 by 10 in w results in another word in the language. We prove that each
prefix normal word is produced in O(n) amortized time, and conjecture, based on
experimental evidence, that the true amortized running time is O(polylog(n)).Comment: 12 pages, 5 figure
Finding class C GPCR subtype-discriminating  n-grams through feature selection

G protein-coupled receptors (GPCRs) are a large and heterogeneous superfamily of receptors that are key cell players for their role as extracellular signal transmitters. Class C GPCRs, in particular, are of great interest in pharmacology. The lack of knowledge about their full 3-D structure prompts the use of their primary amino acid sequences for the construction of robust classifiers, capable of discriminating their different subtypes. In this paper, we describe the use of feature selection techniques to build Support Vector Machine (SVM)-based classification models from selected receptor subsequences described as n-grams. We show that this approach to classification is useful for finding class C GPCR subtype-specific motifs.Peer ReviewedPostprint (author’s final draft
Metagenomic analysis of the saliva microbiome with merlin

In recent years, metagenomics has demonstrated to play an essential role on the study of the microorganisms that live in microbial communities, particularly those who inhabit the human body. Several bioinformatics tools and pipelines have been developed for the analysis of these data, but they usually only address one topic: to identify the taxonomic composition or to address the metabolic functional profile. This work aimed to implement a computational framework able to answer the two questions simultaneously. Merlin, a previously released software aiming at the reconstruction of genome-scale metabolic models for single organisms, was extended to deal with metagenomics data. It has an user-friendly and intuitive interface, being suitable for those with limited bioinformatics skills. The performance of the tool was evaluated with samples from the Human Microbiome Project, particularly from saliva. Overall, the results show the same patterns reported before: while the pathways needed for microbial life remain relatively stable, the community composition varies extensively among individuals
Networking the way towards antimicrobial combination therapies

Publicado em "8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014)"The exploration of new antimicrobial combinations is a pressing concern for Clinical Microbiology due to the growing number of resistant strains emerging in healthcare settings and in the general community. Researchers are screening agents with alternative modes of action and interest is rising for the potential of antimicrobial peptides (AMPs). This work presents the first ever network reconstruction of AMP combinations reported in the literature fighting Pseudomonas aeruginosa infections. The network, containing 193 combinations of AMPs with 39 AMPs and 154 traditional antibiotics, is expected to help in the design of new studies, notably by unveiling different mechanisms of action and helping in the prediction of new combinations and synergisms. The challenges faced in the attempted text-mining approaches and other considerations regarding the manual curation of the data are pointed out, reflecting about the future automation of this type of reconstruction as means to widen the scope of analysis
Evaluating pathway enumeration algorithms in metabolic engineering case studies

The design of cell factories for the production of compounds involves the search for suitable heterologous pathways. Different strategies have been proposed to infer such pathways, but most are optimization approaches with specific objective functions, not suited to enumerate multiple pathways. In this work, we analyze two pathway enumeration algorithms based on graph representations: the Solution Structure Generation and the Find Path algorithms. Both are capable of enumerating exhaustively multiple pathways using network topology. We study their capabilities and limitations when designing novel heterologous pathways, by applying these methods on two case studies of synthetic metabolic engineering related to the production of butanol and vanillin
Bringing named entity recognition on Drupal content management system

Publicado em "8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014)"Content management systems and frameworks (CMS/F) play a key role in Web development. They support common Web operations and provide for a number of optional modules to implement customized functionalities. Given the increasing demand for text mining (TM) applications, it seems logical that CMS/F extend their offer of TM modules. In this regard, this work contributes to Drupal CMS/F with modules that support customized named entity recognition and enable the construction of domain-specific document search engines. Implementation relies on well-recognized Apache Information Retrieval and TM initiatives, namely Apache Lucene, Apache Solr and Apache Unstructured Information Management Architecture (UIMA). As proof of concept, we present here the development of a Drupal CMS/F that retrieves biomedical articles and performs automatic recognition of organism names to enable further organism-driven document screening
Marky: a lightweight web tracking tool for document annotation

Document annotation is an elementary task in the development of Text Mining applications, notably in defining the entities and relationships that are relevant to a given domain. Many annotation software tools have been implemented. Some are particular to a Text Mining framework while others are typical stand-alone tools. Regardless, most development efforts were driven to basic functionality, i.e. performing the annotation, and to interface, making sure operation was intuitive and visually appellative. The deployment of large-scale annotation jamborees and projects showed the need for additional features regarding inter- and intra-annotation management. Therefore, this paper presents Marky, a new Web-based document annotation tool that integrates a highly customisable annotation environment with a robust project management system. Novelty lays on the annotation tracking system, which supports per user and per round annotation change tracking and thus, enables automatic annotation correction and agreement analysis
Designing an ontology tool for the unification of biofilms data

Publicado em "8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014)"The description of biofilm features presents a conceptual and practical challenge. Biofilm studies often encompass multidisciplinary approaches from Biology, Chemistry, Medicine, Material Science and Engineering, among other fields. Standardising biofilm data is essential to be able to accomplish large-scale collaborative and complementary analysis. To define a common standard format to exchange the heterogeneous biofilm data, it is first necessary to define a set of minimum information for the documentation of biofilm experiments. Then, data should be organised and semantically integrated. This paper describes the first ontology designed to share structured vocabulary for the annotation of the general biofilm experimental workflow – the Biofilm Ontology (BO). This ontology is intended for the broad research community, including bench microbiologists, clinical researchers, clinicians, curators and bioinformaticians
BEW: Bioinformatics workbench for analysis of biofilms experimental data

Biofilms research has evolved considerably in the last decade and is now generating large volumes of heterogeneous data. MIABiE, the international initiative on Biofilms, is devising guidelines for data interchange, and some databases provide access to biofilms experiments. However, the field is lacking appropriate bioinformatics tools in support of increasing operational and analytical needs. This paper presents a flexible and extensible open-source workbench for the operation and analysis of biofilms experiments, as follows: (i) the creation of customised experiments, (ii) the collection of various analytical results, following community standardisation guidelines and (iii) on-demand reporting and statistical evaluation
Exploring the Role of Macroeconomic Mechanisms in Voluntary Resource Provisioning in Community Network Clouds

Internet and communication technologies have lowered the costs of enabling individuals and communities to collaborate together. This collaboration has provided new services like user-generated content and social computing, as evident from success stories like Wikipedia. Through collaboration, collectively built infrastructures like community wireless mesh networks where users provide the communication network, have also emerged. Community networks have demonstrated successful bandwidth sharing, but have not been able to extend their collective effort to other computing resources like storage and processing. The success of cloud computing has been enabled by economies of scale and the need for elastic, flexible and on-demand provisioning of computing services. The consolidation of today's cloud technologies offers now the possibility of collectively built community clouds, building upon user-generated content and user-provided networks towards an ecosystem of cloud services. We explore in this paper how macroeconomic mechanisms can play a role in overcoming the barriers of voluntary resource provisioning in such community clouds, by analysing the costs involved in building these services and how they give value to the participants. We indicate macroeconomic policies and how they can be implemented in community networks, to ease the uptake and ensure the sustainability of community clouds