Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.

Event-based visual servoing

Traditional visual servoing systems have been widely studied in the last years. These systems control the position of the camera attached to the robot end-effector guiding it from any position to the desired one. These controllers can be improved by using the event-based control paradigm. The system proposed in this paper is based on the idea of activating the visual controller only when something significant has occurred in the system (e.g. when any visual feature can be loosen because it is going outside the frame). Different event triggers have been defined in the image space in order to activate or deactivate the visual controller. The tests implemented to validate the proposal have proved that this new scheme avoids visual features to go out of the image whereas the system complexity is reduced considerably. Events can be used in the future to change different parameters of the visual servoing systems.

Event-based visual servoing with features’ prediction

Event-based visual servoing is a recently presented approach that performs the positioning of a robot using visual information only when it is required. From the basis of the classical image-based visual servoing control law, the scheme proposed in this paper can reduce the processing time at each loop iteration in some specific conditions. The proposed control method enters in action when an event deactivates the classical image-based controller (i.e. when there is no image available to perform the tracking of the visual features). A virtual camera is then moved through a straight line path towards the desired position. The virtual path used to guide the robot improves the behavior of the previous event-based visual servoing proposal.

LMP triggered real time demand response events

Recent changes in power systems mainly due to the substantial increase of distributed generation and to the operation in competitive environments has created new challenges to operation and planning. In this context, Virtual Power Players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. Demand response market implementation has been done in recent years. Several implementation models have been considered. An important characteristic of a demand response program is the trigger criterion. A program for which the event trigger depends on the Locational Marginal Price (LMP) used by the New England Independent System operator (ISO-NE) inspired the present paper. This paper proposes a methodology to support VPP demand response programs management. The proposed method has been computationally implemented and its application is illustrated using a 32 bus network with intensive use of distributed generation. Results concerning the evaluation of the impact of using demand response events are also presented.

Demand response in future power systems management – a conceptual framework and simulation tool

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Tehohoidon haittatapahtumien herätetyökalun sisällöllinen ja kulttuurinen validointi suomalaiseen tehohoitoon

Tehohoidossa tapahtuu potilaan turvallisuutta vaarantavia tapahtumia, joista voi seurata potilaalle vakavaa haittaa. Haittatapahtumat johtavat inhimillisen kärsimyksen lisääntymiseen, pitkittyneeseen sairaalahoitoon, viivästyneeseen toipumiseen ja pysyvään vammautumiseen tai kuolemaan. Haittatapahtumien raportointi, mittaaminen ja tunnistaminen ovat edellytys potilasturvallisuuden edistämiselle ja korkealaatuiselle hoidolle. Tehohoitoon tarvitaan tehohoidon haittatapahtumat tunnistava menetelmä. Yksi tehokkaaksi ja hyväksi todettu menetelmä on haittatapahtumien herätetyökalu, jonka avulla voidaan tunnistaa potilasasiakirjoista takautuvasti haittaan johtaneita tapahtumia. Tämän tutkimuksen tarkoituksena oli selvittää soveltuuko Yhdysvalloissa kehitetty tehohoidon haittatapahtumien herätetyökalu (IHI ICU Adverse Event Trigger Tool) suomalaiseen tehohoitoon sisällöllisesti ja kulttuurisesti. Tehohoidon haittatapahtumien herätetyökalu valittiin kirjallisuuskatsauksen perusteella. Herätetyökalun käänsi suomeksi kaksi virallista kielenkääntäjää, jonka jälkeen tehohoidon asiantuntijat (N=14) arvioivat käännösten sisällöllistä ja kulttuurista soveltuvuutta suomalaiseen tehohoitoon. Asiantuntijat arvioivat herätetyökalun jokaisen muuttujan merkityksellisyyttä suhteessa tarkoitukseen sisällön validiteetti indeksin avulla. Lisäksi asiantuntijat antoivat kommentteja ja ehdotuksia käännöksiin liittyen. Asiantuntijoiden antamista numeerisista arvioinneista laskettiin yksimielisyysprosentit ja keskiarvot. Kommentit ja ehdotukset ryhmiteltiin analyysirungon mukaisiin luokkiin, jonka jälkeen niistä tiivistettiin ja pelkistettiin oleellinen tehohoidon haittatapahtumien herätetyökaluun liittyvä tieto. Asiantuntijoiden antamien kommenttien ja ehdotusten sekä numeeristen arvioiden mukaan herätteistä (N=24) kymmenen soveltuu (yksimielisyys ≥ 80 %) sisällöllisesti ja kulttuurisesti suomalaiseen tehohoidon haittatapahtumien herätetyökaluun. 12 herätteen suomenkielistä käännöstä voidaan käyttää suomalaisessa haittatapahtumien herätetyökalussa, mutta sen kielellinen vastaavuus ei ole kovin hyvä (yksimielisyys ≥ 40–≤ 80 %) Kahden herätteen käännös ei vastaa suomenkielistä ilmaisua, joten ne eivät sovellu sellaisenaan suomalaiseen tehohoidon haittatapahtumien herätetyökaluun (yksimielisyys ≤ 40 %). Kommenttien ja ehdotusten mukaan tärkeää on, että herätetyökalussa käytetyt termit ja sanat ovat selkeitä ja soveltuvat siihen kulttuuriin, jossa niitä käytetään. Tulosten mukaan tehohoidon haittatapahtumien herätetyökalu soveltuu osittain suomalaiseen tehohoitoon, mutta herätetyökalun muokkaamista, täydentämistä ja päivittämistä tarvitaan. Tutkimuksesta saatuja tuloksia voidaan käyttää hyödyksi kehitettäessä uutta ja paremmin suomalaiseen tehohoitoon soveltuvaa tehohoidon haittatapahtumien herätetyökalua.

Biomedical events extraction using the hidden vector state model

Objective: Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein-protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extraction is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events. Methods and material: HVS has been previously employed for extracting PPIs. In HVS-BioEvent, we propose an automated way to generate abstract annotations for HVS training and further propose novel machine learning approaches for event trigger words identification, and for biomedical events extraction from the HVS parse results. Results: Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP'09 shared task, which is only 2.38% lower than the best performing system by UTurku in the BioNLP'09 shared task. Nevertheless, HVS-BioEvent outperforms UTurku's system on complex events extraction with 36.57% vs. 30.52% being achieved for extracting regulation events, and 40.61% vs. 38.99% for negative regulation events. Conclusions: The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it could naturally model embedded structural context in sentences.

Test vector generation for the phase II ATLAS event filter trigger upgrade

In the near future, the LHC experiments will continue to be upgraded as the LHC luminosity will increase from the design 1034 to 7.5 × 1034, with the HL-LHC project, to reach 3000 × f b−1 of accumulated statistics. After the end of a period of data collection, CERN will face a long shutdown to improve overall performance by upgrading the experiments and implementing more advanced technologies and infrastructures. In particular, ATLAS will upgrade parts of the detector, the trigger, and the data acquisition system. It will also implement new strategies and algorithms for processing and transferring the data to the final storage. This PhD thesis presents a study of a new pattern recognition algorithm to be used in the trigger system, which is a software designed to provide the information necessary to select physical events from background data. The idea is to use the well-known Hough Transform mathematical formula as an algorithm for detecting particle trajectories. The effectiveness of the algorithm has already been validated in the past, independently of particle physics applications, to detect generic shapes in images. Here, a software emulation tool is proposed for the hardware implementation of the Hough Transform, to reconstruct the tracks in the ATLAS Trigger and Data Acquisition system. Until now, it has never been implemented on electronics in particle physics experiments, and as a hardware implementation it would provide overall latency benefits. A comparison between the simulated data and the physical system was performed on a Xilinx UltraScale+ FPGA device.

Lake dwellers occupation gap in Lake Geneva (France-Switzerland) possibly explained by an earthquake-mass movement-tsunami event during Early Bronze Age

High-resolution seismic and sediment core data from the 'Grand Lac' basin of Lake Geneva reveal traces of repeated slope instabilities with one main slide-evolved mass-flow (minimum volume 0.13 km3) that originated from the northern lateral slope of the lake near the city of Lausanne. Radiocarbon dating of organic remains sampled from the top of the main deposit gives an age interval of 1865-1608 BC. This date coincides with the age interval for a mass movement event described in the 'Petit Lac' basin of Lake Geneva (1872-1622 BC). Because multiple mass movements took place at the same time in different parts of the lake, we consider the most likely trigger mechanism to be a strong earthquake (Mw 6) that occurred in the period between 1872 and 1608 BC. Based on numerical simulations, we show the major deposit near Lausanne would have generated a tsunami with local wave heights of up to 6 m. The combined effects of the earthquake and the following tsunami provide a possible explanation for a gap in lake dwellers occupation along the shores of Lake Geneva revealed by dendrochronological dating of two palafitte archaeological sites.

Trigger and data link system for CMS Resistive Plate Chambers at the LHC accelerator

The purpose of the work was to realize a high-speed digital data transfer system for RPC muon chambers in the CMS experiment on CERN’s new LHC accelerator. This large scale system took many years and many stages of prototyping to develop, and required the participation of tens of people. The system interfaces to Frontend Boards (FEB) at the 200,000-channel detector and to the trigger and readout electronics in the control room of the experiment. The distance between these two is about 80 metres and the speed required for the optic links was pushing the limits of available technology when the project was started. Here, as in many other aspects of the design, it was assumed that the features of readily available commercial components would develop in the course of the design work, just as they did. By choosing a high speed it was possible to multiplex the data from some the chambers into the same fibres to reduce the number of links needed. Further reduction was achieved by employing zero suppression and data compression, and a total of only 660 optical links were needed. Another requirement, which conflicted somewhat with choosing the components a late as possible was that the design needed to be radiation tolerant to an ionizing dose of 100 Gy and to a have a moderate tolerance to Single Event Effects (SEEs). This required some radiation test campaigns, and eventually led to ASICs being chosen for some of the critical parts. The system was made to be as reconfigurable as possible. The reconfiguration needs to be done from a distance as the electronics is not accessible except for some short and rare service breaks once the accelerator starts running. Therefore reconfigurable logic is extensively used, and the firmware development for the FPGAs constituted a sizable part of the work. Some special techniques needed to be used there too, to achieve the required radiation tolerance. The system has been demonstrated to work in several laboratory and beam tests, and now we are waiting to see it in action when the LHC will start running in the autumn 2008.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

The topological trigger system of the TOF detector for the ALICE experiment at the LHC

The ALICE experiment at the LHC has been designed to cope with the experimental conditions and observables of a Quark Gluon Plasma reaction. One of the main assets of the ALICE experiment with respect to the other LHC experiments is the particle identification. The large Time-Of-Flight (TOF) detector is the main particle identification detector of the ALICE experiment. The overall time resolution, better that 80 ps, allows the particle identification over a large momentum range (up to 2.5 GeV/c for pi/K and 4 GeV/c for K/p). The TOF makes use of the Multi-gap Resistive Plate Chamber (MRPC), a detector with high efficiency, fast response and intrinsic time resoltion better than 40 ps. The TOF detector embeds a highly-segmented trigger system that exploits the fast rise time and the relatively low noise of the MRPC strips, in order to identify several event topologies. This work aims to provide detailed description of the TOF trigger system. The results achieved in the 2009 cosmic-ray run at CERN are presented to show the performances and readiness of TOF trigger system. The proposed trigger configuration for the proton-proton and Pb-Pb beams are detailed as well with estimates of the efficiencies and purity samples.

Measurement of charged-particle event shape variables in inclusive √(s)=7 TeV proton-proton interactions with the ATLAS detector

The measurement of charged-particle event shape variables is presented in inclusive inelastic pp collisions at a center-of-mass energy of 7 TeV using the ATLAS detector at the LHC. The observables studied are the transverse thrust, thrust minor, and transverse sphericity, each defined using the final-state charged particles' momentum components perpendicular to the beam direction. Events with at least six charged particles are selected by a minimum-bias trigger. In addition to the differential distributions, the evolution of each event shape variable as a function of the leading charged-particle transverse momentum, charged-particle multiplicity, and summed transverse momentum is presented. Predictions from several Monte Carlo models show significant deviations from data.