Marc Fouet

Rémi Courson

Sylviane Baster

Anne-Marie Marie Gué

Anne Marie

INTEGRATED MAGNETOFLU- IDIC DEVICE ENABLING NOVEL ACUTATION FUNCTIONALITIES Gué An

AN INTEGRATED MAGNETOFLUIDIC DEVICE ENABLING NOVEL ACUTATION FUNCTIONALITIES

Keywords: Magnetic actuation, Magnetofluidic, Micro-coils, Focusing

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Magnetic hybrid systems include external permanent magnets for the generation of a high and homogenous magnetic field, and integrated micro-coils locally producing a high gradient. Thinking about all the design possibilities those systems can offer is quite intriguing, and depending on the strength of the homogeneous magnetic field, we can either control single beads, or play with chains and clusters [START_REF] Eickenberg | Continuous-flow particle guiding based on dipolar coupled magnetic superstructures in rotating magnetic fields[END_REF].

Our interest resides in controlling single cells and particles with high precision, and our work on planar coils [START_REF] Fulcrand | On chip magnetic actuator for batch-mode dynamic manipulation of magnetic particles in compact lab-on-chip[END_REF] paved the way regarding the fabrication process and bulk separation functions. Hybrid devices seemed like a promising choice to deal with magnetic actuation at micro scale, that we achieved with two different focusing systems, a shift register and a trapping array.

EXPERIMENTAL

Microfabrication technology consists in a simple two-step process. Glass substrates (500 µm, Schott AF32) were chosen for imaging purposes during experiments.

The micro coils are wires of 5µm width and height, and were obtained with standard micro-technology processes (Ti/Au metallization and electro-plating of copper or gold), then encapsulated with SU8 (10µm) for protection. For the microfluidic channels, two layers of low cost dry film photoresist (20µm, DF-1020, EMS Inc, Ohio) were laminated on top of the micro-coils, and structured with photolithography according to a technique we developed (3D structures can be achieved if piling more layers) [START_REF] Courson | Low cost integration of multilevel lab-on-achip using a new generation of dry film photoresists[END_REF].

Figure 1: a) b) SEM images of microfabricated focusing and shift register units, respectively. c) Optical microscope picture of a cell trapping array (with coils and microfluidic channel). Cells or beads can be trapped on the coils between the pillars.

The permanent magnetic field value in the working zone is tuned by the distance between two permanent magnets (NdFeB, 1.3 T remanence) and the chip, from 40 to 70 mT with this setup. In order to control this distance, we built a chip holder (Fig. 2) that also comprises electrical and fluidic connection features (with a spring loaded connector and a manifold). The experimental setup is based on a fluorescence microscope (with an EMCCD camera), a pressure controller, and we developed an electronic board based on an Arduino/Labview solution to power the coils with positive or negative currents. Solutions of superparamagnetic beads were flowed through the system while recording the motion and trajectories of the beads for a given electrical current. We used 4.5 µm and fluorescent 8 µm diameter beads from Invitrogen and Spherotech respectively.

RESULTS AND DISCUSSION

Focusing 4.5 µm magnetic beads was achieved with two different designs (Fig. 3). Working in a stronger permanent field, we even showed deviation on a small chains of 2-3 beads. Beads velocity in Fig. 3 a) is 560 µm/s, and we observed the same range of deviation at higher velocities (up to 2 mm/s), for a 85 mA current. Magnetic traps are based on the attraction of a middle wire and repulsion (or focusing) of side wires; to release the particle quickly we invert the current polarity for a short amount of time. We demonstrated single bead trapping with 8 µm diameter fluorescent beads (Fig. 4) at velocities above 1 mm/s, as well as trapping (and release) of bigger objects on the same chip (shift register). We therefore proved that cell arrays can be organized dynamically and reversibly.

CONCLUSION

Albeit this study only shows a few design possibilities of magnetic hybrid systems, our results show we are able to achieve key operations like trapping and focusing single magnetic beads (and small chains) at cell resolution. Dynamic cell trapping arrays find applications in co-culture and screening systems, as we intend to design and test new functions as tools for single cell biology experiments. Further developments are needed to explore beads concentration regulation (e.g., with a shift register) or automated capture and sorting of single cells coupled to analysis systems, but this would be the early stage of highly integrated complex magnetofluidic systems.

Figure 2 :

 2 Figure 2: a) CAD drawing of the chip holder setup. Chosen materials were aluminum and PTFE (for the manifold). b) Actual built setup (connected), with mounted magnets and chip. c) Pictures of magnetofluidic chips. Coils are made of copper (left) and gold (right). The red dashed lines represent the magnetic working zone (external field considered to be homogeneous).

Figure 3 :

 3 Figure 3: a) Two coils focusing device performing a deflection on two single beads (bottom) and a chain of 4 beads (top). b) Single coil focusing system centering a single bead. For both images, the electrical current was 85 mA. Red/white arrows point in the direction of the flow.

Figure 4

 4 Figure 4: a) Trajectory of a 8 µm diameter bead being captured and released 4 times with a trapping array. b) Coils actuation and bead velocity for experiment shown in a). c) Trapping of several beads simultaneously (trajectories shown). d) Possibility for the shift register to stop and trap beads and clusters. Red/white arrows point in the direction of the flow. Current of 100 mA was used for all these experiments.

ACKNOWLEDGEMENTS

This study was supported by the French National Research Agency (ANR) in the frame of the PIA -Nanobiotechnology project Digidag.


Danielle L Mowery

Sumithra Velupillai
email: sumithra@dsv.su.se

Brett R South
email: brett.south@hsc.utah.edu

Lee Christensen

David Martinez
email: davidm@csse.unimelb.edu.au

Liadh Kelly

Lorraine Goeuriot

Noemie Elhadad
email: noemie.elhadad@columbia.edu

Sameer Pradhan
email: sameer.pradhan@childrens.harvard.edu

Guergana Savova
email: guergana.savova@childrens.harvard.edu

Wendy W Chapman
email: wendy.chapman@utah.edu

Task 2: ShARe/CLEF eHealth Evaluation Lab 2014

Keywords: Natural Language Processing, Template Filling, Information Extraction, Clinical Text

This paper reports on Task 2 of the 2014 ShARe/CLEF eHealth evaluation lab which extended Task 1 of the 2013 ShARe/CLEF eHealth evaluation lab by focusing on template filling of disorder attributes. The task was comprised of two subtasks: attribute normalization (task 2a) and cue identification (task 2b). We instructed participants to develop a system which either kept or updated a default attribute value for each task. Participant systems were evaluated against a blind reference standard of 133 discharge summaries using Accuracy (task 2a) and F-score (task 2b). In total, ten teams participated in task 2a, and three teams in task 2b. For task 2a and 2b, the HITACHI team systems (run 2) had the highest performances, with an overall average average accuracy of 0.868 and F1-score (strict) of 0.676, respectively.

Introduction

In recent years, healthcare initiatives such as the United States Meaningful Use [START_REF]Medicaid Services: Eligible professional meaningful use menu set measures: Measure 5 of 10[END_REF] and European Union Directive 2011/24/EU [2] have created policies and legislation to promote patient involvement and understanding of their personal health information. These policies and legislation have encouraged health care DLM, SV, WWC led the task, WWC, SV, DLM, NE, SP, and GS defined the task, SV, DLM, BRS, LC, and DM processed and distributed the dataset, and SV, DLM, and DM led result evaluations organizations to provide patients open access to their medical records and advocate for more patient-friendly technologies. Patient-friendly technologies that could help patients understand their personal health information, e.g., clinical reports, include providing links for unfamiliar terms to patient-friendly websites and generating patient summaries that use consumer-friendly terms and simplified syntactic constructions. These summaries could also limit the semantic content to the most salient events such as active disorder mentions and their related discharge instructions. Natural Language Processing (NLP) can help by filtering non-active disorder mentions using their semantic attributes e.g., negated symptoms (negation) or uncertain diagnoses (certainty) [START_REF] Mowery | Semantic annotation of clinical events for generating a problem list[END_REF] and by identifying the discharge instructions using text segmentation [START_REF] Apostolova | Automatic segmentation of clinical texts[END_REF][START_REF] Engel | Patient understanding of emergency department discharge summary instructions: Where are knowledge deficits greatest?[END_REF].

In previous years, several NLP shared tasks have addressed related semantic information extraction tasks such as automatically identifying conceptsproblems, treatments, and tests -and their related attributes (2010 i2B2/VA Challenge [START_REF] Uzuner | Semantic relations for problemoriented medical records[END_REF]) as well as identifying temporal relationships between these clinical events (2012 i2B2/VA Challenge [START_REF] Sun | Evaluating temporal relations in clinical text: 2012 i2b2 Challenge[END_REF]). The release of these semanticallyannotated datasets to the NLP community is important for promoting the development and evaluation of automated NLP tools. Such tools can identify, extract, filter and generate information from clinical reports that assist patients and their families in understanding the patient's health status and their continued care. The ShARe/CLEF eHealth 2014 shared task [START_REF] Kelly | Overview of the share/clef ehealth evaluation lab[END_REF] focused on facilitating understanding of information in narrative clinical reports, such as discharge summaries, by visualizing and interactively searching previous eHealth data (Task 1) [START_REF] Suominen | Task 1 of the CLEF eHealth Evaluation Lab 2014: visual-interactive search and exploration of eHealth data[END_REF], identifying and normalizing disorder attributes (Task 2), and retrieving documents from the health and medicine websites for addressing questions monoand multi-lingual patients may have about the disease/disorders in the clinical notes (Task 3) [START_REF] Goeuriot | ShARe/CLEF eHealth Evaluation Lab 2014, Task 3: User-centred health information retrieval[END_REF]. In this paper, we discuss Task 2: disorder template filling.

Methods

We describe the ShARe annotation schema, the dataset, and the evaluation methods used for the ShARe/CLEF eHealth Evaluation Lab Task 2.

ShARe Annotation Schema

As part of the ongoing Shared Annotated Resources (ShARe) project [START_REF] Elhadad | The ShARe schema for the syntactic and semantic annotation of clinical texts[END_REF], disorder annotations consisting of disorder mention span offsets, their SNOMED CT codes, and their contextual attributes were generated for community distribution. For 2013 ShARe/CLEF eHealth Challenge Task 1[12] the disorder mention span offsets and SNOMED CT codes were released. For 2014 ShARe/CLEF eHealth Challenge Task 2, we released the disorder templates with 10 attributes that represent a disorder's contextual description in a report including Negation Indicator, Subject Class, Uncertainty Indicator, Course Class, Severity Class, Conditional Class, Generic Class, Body Location, DocTime Class, and Temporal Expression. Each attribute contained two types of annotation values: normalization and cue detection value. For instance, if a disorder is negated e.g., "denies nausea", the Negation Indicator attribute would represent nausea with a normalization value: yes indicating the presence of a negation cue and cue value: start span-end span for denies. All attributes contained a slot for a cue value with the exception of the DocTime Class. Each note was annotated by two professional coders trained for this task, followed by an open adjudication step.

From the ShARe guidelines [START_REF]ShARe CLEF eHealth website task 2 information extraction[END_REF], each disorder mention contained an attribute cue as a text span representing a non-default normalization value (*default normalization value) [START_REF] Kelly | Overview of the share/clef ehealth evaluation lab[END_REF]:

Negation

Dataset

At the time of the challenge, the ShARe dataset consisted of 433 de-identified clinical reports sampled from over 30,000 ICU patients stored in the MIMIC (Multiparameter Intelligent Monitoring in Intensive Care) II database [START_REF] Saeed | MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring[END_REF]. The initial development set contained 300 documents of 4 clinical report typesdischarge summaries, radiology, electrocardiograms, and echocardiograms. The unseen test set contained 133 documents of only discharge summaries. Participants were required to participate in Task 2a and had the option to participate in Task 2b.

For Task 2a and 2b, the dataset contained templates in a "|" delimited format with: a) the disorder CUI assigned to the template as well as the character boundary of the named entity, and b) the default values for each of the 10 attributes of the disorder. Each template contained the following format In this example, the Subject Class cue span is not annotated in ShARe since *patient is an attribute default.

Participant Recruitment and Registration

We recruited participants using listservs such as AMIA NLP Working Group, AISWorld, BioNLP, TREC, CLEF, Corpora, NTCIR, and Health Informatics World. Although the ShARe dataset is de-identified, it contains sensitive, patient information. After registration for task 2 through the CLEF Evaluation Lab, each participant completed the following data access procedure, which included (1) a CITI [15] or NIH [START_REF]National Institute of Health -ethics training module[END_REF] Training certificate in Human Subjects Research, (2) registration on the Physionet.org site [START_REF]Physionet: Physionet site[END_REF], (3) signing a Data Use Agreement to access the MIMIC II data.

Evaluation Metrics

For Tasks 2a and 2b, we determined system performance by comparing participating system outputs against reference standard annotations. We evaluated overall system performance and performance for each attribute type e.g., Negation Indicator.

Task 2a: Normalization Since we defined all possible normalized values for each attribute, we calculated system performance using Accuracy as Accuracy = count of correct normalized values divided by total count of disorder templates.

Task 2b: Cue Detection Since the number of strings not annotated as attribute cues (i.e., true negatives (TN)) is very large, we followed [START_REF] Hripcsak | Agreement, the F-measure, and reliability in information retrieval[END_REF] in calculating F1-score as a surrogate for kappa. F1-score is the harmonic mean of recall and precision, calculated from true positive, false positive, and false negative annotations, which were calculated as follows:

true positive (TP) = the annotation cue span from the participating system overlapped with the annotation cue span from the reference standard false positive (FP) = an annotation cue span from the participating system did not exist in the reference standard annotations false negative (FN) = an annotation cue span from the reference standard did not exist in the participating system annotations

Results

Participating teams included between 1-4 people and competed from Canada (team GRIUM), France (team LIMSI), Germany (teams HPI and DFKI-Medical), India (teams RelAgent and HITACHI), Japan (team HITACHI), Portugal (team UEvora), Taiwan (team ASNLP), Vietnam (team HCMUS) and USA (team CORAL). Participants represented academic and industrial institutions including LIMSI-CNRS, University of Alabama at Birmingham, Hasso Plattner Institute, University of Heidelberg, Academia Sinica, DIRO, University of Science, RelAgent Tech Pvt Ltd, University of Evora, Hitachi, International Institute of Information Technology, and German Research Center for AI (DFKI). In total, ten teams submitted systems for Task 2a. Four teams submitted two runs. For Task 2b, three teams submitted systems, one of them submitted two runs.

System Performance on Task 2a

As shown in Table 1, the HITACHI team system (run 2) had the highest performance in Task 2a, with an overall average accuracy of 0.868. For the individual attributes, team HITACHI had the highest performance for Negation Indicator (0.969), Uncertainty Indicator (0.960), Course Class (0.971), Severity Class (0.982), Conditional Class (0.978), Body Location (0.797) and DocTime Class (0.328), Tables 2 and3. The HCMUS team had the highest performance for the attribute Subject Class (0.995), and three teams (HPI, RelAgent, Coral) had the highest performance for the attribute Temporal Expression (0.864). For the attribute Generic Class, most teams correctly predicted no change in the normalization value.

System Performance on Task 2b

For Task 2b, the HITACHI team system (run 2) had the highest performance, with an overall average F1-score (strict) of 0.676 (Table 4). Team HITACHI also had the highest performance (strict) for the individual attributes Negation Indicator (0.913), Uncertainty Indicator (0.9561), Course Class (0.645), Severity Class (0.847), Conditional Class (0.638), Generic Class (0.225) and Body Location (0.854). The HCMUS team had the highest performance for the attribute Subject Class (0.857), and Temporal Expression (0.287).

Discussion

We released an extended ShARe corpus through Task 2 of the ShARe/CLEFeHealth Evaluation Lab. This corpus contains disease/disorder templates with ten semantic attributes. In the evaluation lab, we evaluated systems on the task of normalizing semantic attribute values overall and by attribute type (Task 2a), as well as on the task of assigning attribute cue slot values (Task 2b). This is a unique clinical NLP challenge -no previous challenge has targeted such rich semantic annotations. Results show that high overall average accuracy can be achieved by NLP systems on the task of normalizing semantic attribute values, but that performance levels differ greatly between individual attribute types, which was also reflected in the results for cue slot prediction (Task 2b). This corpus and the participating team system results are an important contribution to the research community and the focus on rich semantic information is unprecedented.

  [12]: DD DocName|DD Spans|DD CUI|Norm NI|Cue NI| Norm SC|Cue SC|Norm UI|Cue UI|Norm CC|Cue CC| Norm SV|Cue SV|Norm CO|Cue CO|Norm GC|Cue GC| Norm BL|Cue BL|Norm DT|Norm TE|Cue TE For example, the following sentence, "The patient has an extensive thyroid history.", was represented to participants with the following disorder template with default normalization and cue values: 09388-093839-DISCHARGE SUMMARY.txt|30-36|C0040128|*no|*NULL| patient|*NULL|*no|*NULL|*false|*NULL| unmarked|*NULL|*false|*NULL|*false|*NULL| NULL|*NULL|*Unknown|*None|*NULL For Task 2a: Normalization, participants were asked to either keep or update the normalization values for each attribute. For the example sentence, the Task 2a changes: 09388-093839-DISCHARGE SUMMARY.txt|30-36|C0040128|*no|*NULL| patient|*NULL|*no|*NULL|*false|*NULL| unmarked|*NULL|severe|*NULL|*false|*NULL| C0040132|*NULL|Before|*None|*NULL For Task 2b: Cue detection, participants were asked to either keep or update the cue values for each attribute. For the example sentence, the Task 2b changes: 09388-093839-DISCHARGE SUMMARY.txt|30-36|C0040128|*no|*NULL| patient|*NULL|*no|*NULL|*false|*NULL| unmarked|*NULL|severe|20-28|*false|*NULL| C0040132|30-36|Before|*None|*NULL

  Generic Class (GC): def. indicates a generic mention of disorder: *false, true Ex. "Vertigo while walking." DocTime Class (DT): def. indicates temporal relation between a disorder and document authoring time: before, after, overlap, before-overlap, *unknown Ex. "Stroke in 1999." Temporal Expression (TE): def. represents any TIMEX (TimeML) temporal expression related to the disorder: *none, date, time, duration, set Ex. "Flu on March 10."

	Indicator (NI): def. indicates a disorder was negated: *no, yes
	Ex. "No cough."
	Subject Class (SC): def. indicates who experienced a disorder: *patient,
	family member, donor family member, donor other, null, other
	Ex. "Dad had MI."
	Uncertainty Indicator (UI): def. indicates a measure of doubt about the
	disorder: *no, yes
	Ex. "Possible pneumonia."
	Course Class (CC): def. indicates progress or decline of a disorder: *un-
	marked, changed, increased, decreased, improved, worsened, resolved
	Ex. "Bleeding abated."
	Body Location (BL): def. represents an anatomical location: *NULL, CUI:
	C0015450, CUI-less
	Ex. "Facial lesions."

Severity Class (SV): def. indicates how severe a disorder is: *unmarked, slight, moderate, severe Ex. "Infection is severe." Conditional Class (CO): def. indicates existence of disorder under certain circumstances: *false, true Ex. "Return if nausea occurs."

Table 1 :

 1 System Performance, Task 2a: predict each attribute's normalization slot value.

	Accuracy: overall (official ranking result)
	Attribute System ID ({team}.{system}) Accuracy
	Overall TeamHITACHI.2	0.868
	Average TeamHITACHI.1	0.854
	RelAgent.2	0.843
	RelAgent.1	0.843
	TeamHCMUS.1	0.827
	DFKI-Medical.2	0.822
	LIMSI.1	0.804
	DFKI-Medical.1	0.804
	TeamUEvora.1	0.802
	LIMSI.2	0.801
	ASNLP.1	0.793
	TeamCORAL.1.add	0.790
	TeamGRIUM.1	0.780
	HPI.1		0.769
	Recall =
		T P (T P + F N )	(1)
	Precision =
		T P (T P + F P )	(2)
	F1-score =
	2	(Recall * P recision) (Recall + P recision)

Table 2 :

 2 System Performance, Task 2a: predict each attribute's normalization slot value. Accuracy per attribute type -Attributes Negation Indicator, Subject Class, Uncertainty Indicator, Course Class, Severity Class, Conditional Class.

	Attribute System ID	Accuracy Attribute System ID	Accuracy
	Negation	TeamHITACHI.2	0.969	Subject	TeamHCMUS.1	0.995
	Indicator	RelAgent.2	0.944	Class	TeamHITACHI.2	0.993
		RelAgent.1	0.941		TeamHITACHI.1	0.990
		TeamASNLP	0.923		TeamUEvora.1	0.987
		TeamGRIUM.1	0.922		DFKI-Medical.1	0.985
		TeamHCMUS.1	0.910		DFKI-Medical.2	0.985
		LIMSI.1	0.902		LIMSI.1	0.984
		LIMSI.2	0.902		RelAgent.2	0.984
		TeamUEvora.1	0.901		RelAgent.1	0.984
		TeamHITACHI.1	0.883		LIMSI.2	0.984
		DFKI-Medical.2	0.879		TeamHPI	0.976
		DFKI-Medical.1	0.876		TeamCORAL.1.add 0.926
		TeamCORAL.1.add 0.807		TeamASNLP	0.921
		TeamHPI	0.762		TeamGRIUM.1	0.611
	Uncertainty TeamHITACHI.1	0.960	Course	TeamHITACHI.2	0.971
	Indicator	RelAgent.2	0.955	Class	TeamHITACHI.1	0.971
		RelAgent.1	0.955		RelAgent.1	0.970
		TeamUEvora.1	0.955		RelAgent.2	0.967
		TeamCORAL.1.add 0.941		TeamGRIUM.1	0.961
		DFKI-Medical.1	0.941		TeamCORAL.1.add 0.961
		DFKI-Medical.2	0.941		TeamASNLP	0.953
		TeamHITACHI.2	0.924		TeamHCMUS.1	0.937
		TeamGRIUM.1	0.923		DFKI-Medical.1	0.932
		TeamASNLP	0.912		DFKI-Medical.2	0.932
		TeamHPI	0.906		TeamHPI	0.899
		TeamHCMUS.1	0.877		TeamUEvora.1	0.859
		LIMSI.1	0.801		LIMSI.1	0.853
		LIMSI.2	0.801		LIMSI.2	0.853
	Severity	TeamHITACHI.2	0.982	Conditional TeamHITACHI.1	0.978
	Class	TeamHITACHI.1	0.982	Class	TeamUEvora.1	0.975
		RelAgent.2	0.975		RelAgent.2	0.963
		RelAgent.1	0.975		RelAgent.1	0.963
		TeamGRIUM.1	0.969		TeamHITACHI.2	0.954
		TeamHCMUS.1	0.961		TeamGRIUM.1	0.936
		DFKI-Medical.1	0.957		LIMSI.1	0.936
		DFKI-Medical.2	0.957		TeamASNLP	0.936
		TeamCORAL.1.add 0.942		LIMSI.2	0.936
		TeamUEvora.1	0.919		TeamCORAL.1.add 0.936
		TeamHPI	0.914		DFKI-Medical.1	0.936
		TeamASNLP	0.912		DFKI-Medical.2	0.936
		LIMSI.1	0.900		TeamHCMUS.1	0.899
		LIMSI.2	0.900		TeamHPI	0.819

Table 3 :

 3 System Performance, Task 2a: predict each attribute's normalization slot value. Accuracy per attribute type -Attributes Generic Class, Body Location, DocTime Class and Temporal Expression.

	Attribute System ID	Accuracy Attribute System ID	Accuracy
	Generic TeamGRIUM.1	1.000	Body	TeamHITACHI.2	0.797
	Class	LIMSI.1	1.000	Location TeamHITACHI.1	0.790
		TeamHPI	1.000		RelAgent.2	0.756
		TeamHCMUS.1	1.000		RelAgent.1	0.753
		RelAgent.2	1.000		TeamGRIUM.1	0.635
		TeamASNLP	1.000		DFKI-Medical.2	0.586
		RelAgent.1	1.000		TeamHCMUS.1	0.551
		LIMSI.2	1.000		TeamASNLP	0.546
		TeamUEvora.1	1.000		TeamCORAL.1.add 0.546
		DFKI-Medical.1	1.000		TeamUEvora.1	0.540
		DFKI-Medical.2	1.000		LIMSI.1	0.504
		TeamHITACHI.2	0.990		LIMSI.2	0.504
		TeamCORAL.1.add 0.974		TeamHPI	0.494
		TeamHITACHI.1	0.895		DFKI-Medical.1	0.486
	DocTime TeamHITACHI.2	0.328	Temporal TeamHPI	0.864
	Class	TeamHITACHI.1	0.324	Expression RelAgent.2	0.864
		LIMSI.1	0.322		RelAgent.1	0.864
		LIMSI.2	0.322		TeamCORAL.1.add 0.864
		TeamHCMUS.1	0.306		TeamUEvora.1	0.857
		DFKI-Medical.1	0.179		DFKI-Medical.2	0.849
		DFKI-Medical.2	0.154		LIMSI.1	0.839
		TeamHPI	0.060		TeamHCMUS.1	0.830
		TeamGRIUM.1	0.024		TeamASNLP	0.828
		RelAgent.2	0.024		TeamGRIUM.1	0.824
		RelAgent.1	0.024		LIMSI.2	0.806
		TeamUEvora.1	0.024		TeamHITACHI.2	0.773
		TeamASNLP	0.001		TeamHITACHI.1	0.766
		TeamCORAL.1.add 0.001		DFKI-Medical.1	0.750

Table 4 :

 4 System Performance, Task 2b: predict each attribute's cue slot value. Strict and Relaxed F1-score, Precision and Recall (overall and per attribute type)

	Attribute System ID		Strict		Relaxed
			F1-score Precision Recall F1-score Precision Recall
	Overall	TeamHITACHI.2 0.676	0.620	0.743 0.724	0.672	0.784
	Average	TeamHITACHI.1 0.671	0.620	0.731 0.719	0.672	0.773
		TeamHCMUS.1 0.544	0.475	0.635 0.648	0.583	0.729
		HPI.1	0.190	0.184	0.197 0.323	0.314	0.332
	Negation	TeamHITACHI.2 0.913	0.955	0.874 0.926	0.962	0.893
	Indicator	TeamHITACHI.1 0.888	0.897	0.879 0.905	0.912	0.897
		TeamHCMUS.1 0.772	0.679	0.896 0.817	0.735	0.919
		HPI.1	0.383	0.405	0.363 0.465	0.488	0.444
	Subject	TeamHCMUS.1 0.857	0.923	0.800 0.936	0.967	0.907
	Class	TeamHITACHI.1 0.125	0.068	0.760 0.165	0.092	0.814
		TeamHITACHI.2 0.112	0.061	0.653 0.152	0.085	0.729
		HPI.1	0.106	0.059	0.520 0.151	0.086	0.620
	Uncertainty TeamHITACHI.2 0.561	0.496	0.647 0.672	0.612	0.746
	Indicator	TeamHITACHI.1 0.514	0.693	0.408 0.655	0.802	0.553
		TeamHCMUS.1 0.252	0.169	0.494 0.386	0.275	0.646
		HPI.1	0.166	0.106	0.376 0.306	0.209	0.572
	Course	TeamHITACHI.1 0.645	0.607	0.689 0.670	0.632	0.712
	Class	TeamHITACHI.2 0.642	0.606	0.682 0.667	0.632	0.705
		TeamHCMUS.1 0.413	0.316	0.594 0.447	0.348	0.628
		HPI.1	0.226	0.153	0.435 0.283	0.196	0.510
	Severity	TeamHITACHI.2 0.847	0.854	0.839 0.850	0.857	0.843
	Class	TeamHITACHI.1 0.843	0.845	0.841 0.847	0.848	0.845
		TeamHCMUS.1 0.703	0.665	0.746 0.710	0.672	0.752
		HPI.1	0.364	0.306	0.448 0.396	0.336	0.483
	Conditional TeamHITACHI.1 0.638	0.744	0.559 0.801	0.869	0.743
	Class	TeamHITACHI.2 0.548	0.478	0.643 0.729	0.669	0.800
		TeamHCMUS.1 0.307	0.225	0.484 0.441	0.340	0.625
		HPI.1	0.100	0.059	0.315 0.317	0.209	0.658
	Generic	TeamHITACHI.1 0.225	0.239	0.213 0.304	0.320	0.289

Acknowledgments

We greatly appreciate the hard work and feedback of our program committee members. We also want to thank all participating teams. This shared task was partially supported by the CLEF Initiative, the ShARe project funded by the United States National Institutes of Health (R01GM090187), the US Office of the


Thibault Lafont

Nicolas Totaro

Alain Le Bot

A study of the assumptions used in statistical energy analysis

Keywords: Statistical energy analysis, coupling power proportionality, indirect coupling, coupling loss factors

This paper is a discussion on the coupling strength, the frequency bandwidth and indirect coupling loss factor in statistical energy analysis. The example of coupled oscillators is taken. First, it is shown that the coupling power proportionality is always valid for two coupled oscillators for any strength of coupling as long as the resonant modes are within the frequency band. Then, the case of three coupled oscillators indicated that the apparition of indirect coupling loss factors depends on the coupling strength. Finally, it is shown that modes outside the frequency bandwidth of excitation can lead to invalid the reciprocity relation. Some test cases are presented to illustrate these conclusions.

INTRODUCTION

Statistical energy analysis (SEA) is a well-known statistical theory of vibroacoustics which evaluate energy transfers between subsystems subjected to random forces. The main result is the so-called coupling power proportionality (CPP) which states that the power transmitted between two subsystems is proportional to the difference of their modal energies. The proof of this relation is based on the modal approach of SEA where the modes are viewed as mechanical oscillators. The basic model is composed of two coupled oscillators conservatively coupled and submitted to random and uncorrelated external forces having the power spectral density of a white noise [START_REF] Lyon | Power flow and energy sharing in random vibration[END_REF][START_REF] Lyon | Theory and application of Statistical Energy Analysis[END_REF]. A generalization to an arbitrary number of oscillators has been achieved by Newland (3) but with the additional hypothesis that the coupling is weak. A review of the hypotheses useful in SEA is presented in reference [START_REF] Lafont | Review of statistical energy analysis hypotheses in vibroacoustics[END_REF]. Mace (5) considers two types of systems: the one which fully respect SEA hypotheses and where the CPP applies (proper-SEA) and systems which approach CPP introducing indirect coupling loss factors (quasi-SEA). These indirect coupling loss factors appear, for some cases, in the coupling loss factor matrix even for not physically connected subsystems.

This paper is in the continuation of what has been previously done in (4) and has two objectives. First, studying the coupling power proportionality when the hypothesis of weak coupling is violated. Then, showing what could happen when taking non-resonant modes in the frequency bandwidth of integration. For that a reference calculation which makes a link between the power injected and the total energy is done on the example of three coupled oscillators. Then it is compared to the coupling power proportionality given by SEA.

REFERENCE CALCULATION

In this section, the total energies of each oscillator and the power transmitted between them are calculated. These expressions are useful to be compared with SEA coupling power proportionality.

Motion of three oscillators

Let us consider three oscillators coupled by elastic couplings (Fig 1).

In the Fourier space, the equations of motion of three oscillators coupled by elastic couplings are DX=F where X is the displacement vector, F the force vector and D the dynamic stiffness matrix. The frequency response function H j i is the response of oscillator i when a harmonic force is applied to oscillator j. This is

H =    m 1 (ω 2 1 -ω 2 + jω∆ 1 ) -K 1 0 -K 1 m 2 (ω 2 2 -ω 2 + jω∆ 2 ) -K 2 0 -K 2 m 3 (ω 2 3 -ω 2 + jω∆ 3 )    -1 (1)
where

ω i = ((k i + K i )/m i ) 1/2
is the blocked natural angular frequency of oscillator i.

Total energies of oscillators 2.3 Vibrational energies

When the forces are random, stationary and uncorrelated and denoting S j the power spectral density of the random process F j , the expectation of the square of velocity of oscillator i is

< Ẋ2 i >= 1 2π ∫ +∞ -∞ ω 2 ∑ j |H j i | 2 S j dω (2)
where < . > denotes a probability expectation. Let us consider that the forces have a power spectral density constant (S j = 1) in the bandwidth [ω min , ω max ] and zero elsewhere. The vibrational energy defined as twice the kinetic energy is

< E i >= 1 π ∫ ω max ω min m i ω 2 ( ∑ j |H j i | 2 S j ) dω (3)
This expression is valid for any combination of random, stationary and uncorrelated forces of power spectral density S j .

Transmitted power

The net power transmitted from oscillator i to oscillator j when the force is applied to oscillator k is

< P i j >= 1 2 K i j (< X i Ẋj > -< X j Ẋi >) = 1 2π ∫ ω max ω min ℜ[iK i j ω ( ∑ k H k * i H k j S k ) ]dω (4)
where * denotes the complex conjugate and ℜ the real part. Since oscillators 1 and 3 are not directly connected < P 13 >= 0.

Injected power

The power injected into oscillator i is,

< P i in j >=< F i Ẋi >= 1 2π ℜ ∫ ω max ω min iωH i i S i dω (5)
A simpler expression of the injected power is obtained by Gersch [START_REF] Gersch | Average power and power exchange in oscillators[END_REF]. For a white noise excitation (S i = 1 over an infinite frequency band), the injected power i is inversely proportional to the mass m i .

< P i in j >= S i 2m i (6)

SEA CALCULATION

In this section, the expression of the coupling power proportionality and notably the expression of beta factor are shown.

Direct SEA approach: the coupling power proportionality

Assuming that the coupling between two oscillators is conservative and that the external forces are uncorrelated white noises it is proved by Lyon and Maidanik (1), Lyon and Sharton [START_REF] Scharton | Power flow and energy sharing in random vibration[END_REF] and Lotz and Crandall (8) that the power transmitted is proportional to the difference of the total energies. The coupling power proportionality between oscillators i and j can then be written

P i j = η i j ω c (E i -E j ) (7)
Where the coefficient η i j ω c is, for an elastic coupling given by

η i j ω c = K 2 i (∆ 1 + ∆ 2 ) m i m j [(ω 2 i -ω 2 j ) 2 + (∆ i + ∆ j )(∆ i ω 2 j -∆ j ω 2 i )] (8)
where ∆ i = λ i /m i is the half power bandwidth, λ i the viscous damping coefficient, m i the mass, K i the cou- pling stiffness and ω i the natural frequency of oscillator i. The coupling loss factors verify the reciprocity relationship,

η i j ω c = η ji ω c (9)
and will be referred to as consistency condition. In the case of three coupled oscillators the SEA system traducing the CPP is

   P 1 in j P 2 in j P 3 in j    = ω c    η 1 + η 12 -η 21 0 -η 12 η 2 + η 21 + η 23 -η 32 0 -η 23 η 3 + η 32       E 1 E 2 E 3    (10)
Such a system is called proper-SEA system. The conditions of conservation of energy and consistency are verified and the indirect coupling loss factors (η 13 and η 31 ) are null.

ENERGY INFLUENCE COEFFICIENT

The energy influence coefficient method introduced by Guyader (9, 10) is used to compute the indirect coupling loss factors. The oscillators are excited one after another by uncorrelated forces. Three systems are obtained for each excitation giving the total energies of each oscillator. Let us call E j i the vibrational energy of oscillator i when the excitation concerns oscillator j. Superposing each equations allows to build a matrix system which has the form F = Eη where F is the power injected vector, E the matrix composed of vibrational energies E j i and η the vector which contains the damping loss factors, the coupling loss factors and the indirect coupling loss factors. For three oscillators the matrix system writes,

                P 1 in j 0 0 0 P 2 in j 0 0 0 P 3 in j                 = ω c                 E 1 1 E 1 1 E 1 1 -E 1 2 0 0 -E 1 3 0 0 0 -E 1 1 0 E 1 2 E 1 2 E 1 2 0 -E 1 3 0 0 0 -E 1 1 0 0 -E 1 2 E 1 3 E 1 3 E 1 3 E 2 1 E 2 1 E 2 1 -E 2 2 0 0 -E 2 3 0 0 0 -E 2 1 0 E 2 2 E 2 2 E 2 2 0 -E 2 3 0 0 0 -E 2 1 0 0 -E 2 2 E 2 3 E 2 3 E 2 3 E 3 1 E 3 1 E 3 1 -E 3 2 0 0 -E 3 3 0 0 0 -E 3 1 0 E 3 2 E 3 2 E 3 2 0 -E 3 3 0 0 0 -E 3 1 0 0 -E 3 2 E 3 3 E 3 3 E 3 3                                 η 1 η 12 η 13 η 21 η 2 η 23 η 31 η 32 η 3                 (11)

RESULTS AND DISCUSSION

Influence of the coupling strength

Let us consider the oscillators having characteristics as shown in Table 1. The computation of the total energies follows Eq (3). The coupling stiffness denoted K 1 = K 2 vary from 98E -5 N/m to 98E + 5 N/m. The forces have a power spectral density constant (S i = 1) in the time-frequency bandwidth [ω min , ω max ] and zero elsewhere. All modes are resonant (natural frequency within [ω min , ω max ]).

The parameters presented in Table 1 are alike giving close natural frequencies ( f 1 =241.77Hz; f 2 =238.75Hz and f 3 =251.64Hz when K i << k i ). For high coupling values, the natural frequency of each oscillator is affected and can increase. Then, in order to detect the resonant mode for each values of coupling the frequency bandwidth of integration is taken sufficiently large [0; 20 kHz].

Case of two coupled oscillators

Let us consider the case of two oscillators taking the coupling stiffness K 2 = 0 between oscillators 2 and 3.

The expression of the proportionality constant η 12 ω c given by SEA (Eq (8)) is called β SEA and is directly compared to the ratio (called β REF ) of the power transmitted given by Eq (4) and the difference of the total energies (Eq (3)): 2 shows the evolution of beta estimated by SEA and beta estimated by the reference calculation versus the coupling strength. A perfect agreement is observed for any strength of coupling illustrating that the coupling power proportionality is always valid for two oscillators.

β REF = P 12 /(E 1 -E 2 ). Figure

Case of three coupled oscillators

Let us consider the case of three oscillators (K 2 ̸ = 0). As before β i j,REF , computed from Eq (3) and ( 4) is compared with the SEA beta factor (Eq (8)) while the couplings K 1 = K 2 vary. Figure 3 presents the evolution of the beta factors. The gap between β i j,REF and β i j,SEA do not have the same rough size compare to their evolutions. That's why results are presented in two scales: linear (Fig. 3 (a)) and logarithmic (Fig. 3 (b)).

For weak couplings (K 1 << k 1 and K 2 << k 2 ), β i j,REF are equal to β i j,SEA in particular until K 1 =K 2 =98E -1 N/m. For stronger couplings (K 1 ≥ 98E + 1 N/m) it is shown (Fig. 3 a) that β i j,REF and β i j,SEA do not fit traducing that the coupling power proportionality is no longer valid.

In Figure 4, energy influence coefficient method has been used to compute the damping loss factor (DLF), the coupling loss factors (CLF) and the possible indirect coupling loss factors (CLFi) when the couplings K 1 and K 2 grows. Table 2 gives each element of vector η (Eq (11)).

The first observation is that reciprocity relations holds whatever the couplings values are. Indeed, η 12 Figure 3 -Evolution of β i j,REF and β i j,SEA for three oscillators when oscillator 1 is excited versus the coupling strength

K 1 = K 2 .
Figure 4 -Evolution of the CLF, DLF and CLFi versus the couplings stiffness K 1 =K 2 with large bandwidth. = η 21 ; η 23 = η 32 . The second observation is that the damping loss factors are well estimated compared to the prescribed values (see Table 1). The third observation is that, for weak couplings, indirect coupling loss factors values are very low compared to the coupling loss factor values (η ik << η i j << η i ) and thus can be neglected. Conversely, they appear when the couplings are strong (above K 1 =K 2 =98E -1 N/m) and cannot be neglected anymore.

K 98E -5 98E -3 98E -1 98E + 1 98E + 3 98E + 5

Study of the influence of a non resonant mode

In this section, the influence of a non resonant mode is viewed. For that, oscillator's parameters are fixed in such a way that the natural frequency of oscillator 2 is outside the frequency band. Then, the mass of oscillator 2 is adjusted to get closer to the bandwidth of integration. The margin between the natural frequency of oscillator 2 and the bandwidth is evaluated by a simple difference: f 2f min . Three cases are observed :

• f 2 is outside the bandwidth with f 2 < f min . The mode of oscillator 2 is denoted non-resonant low frequency. • f 2 is within the bandwidth with f min < f 2 < f max . The mode of oscillator 2 is resonant.

• f 2 is outside the bandwidth with f max < f 2 . The mode of oscillator 2 is denoted non-resonant high frequency. Figure 5 illustrates by a drawing the simulation protocol. The damping loss factors are prescribed equal for the three oscillators (see Table 3). The couplings are weak (K 1 = K 2 = 98E -3 N/m) the bandwidth of integration is an octave defined by the central frequency f c = 250 Hz. In this way, the resonance pic of oscillator 2 is within the frequency band from the value f 2f min = 0 to f 2f min = 177 Hz. Energy influence coefficient method is again used to estimate the DLF, the CLF and the CLFi. Figure 6 presents the DLF, the CLF and the CLFi while the natural frequency of oscillator 2 varies. First, it is observed that the DLF η 1 and η 3 are correctly estimated. η 2 grows with the shifting of the natural frequency f 2 but correct values are estimated when the resonance pic is within the bandwidth of integration. The CLF are no more symmetric (η i j ̸ = η ji ) when f 2 -f min is below zero and greater than 177 Hz (the resonance frequency is before or after the bandwidth of integration). Nevertheless, the equality becomes valid (η i j = η ji ) when f 2 is within the bandwidth.

The indirect coupling loss factors are symmetric (η 13 = η 31 ) and can have negative values. Their evolu- tions are small compared to the evolution of the CLF and their values are 10 5 times lower than the CLF, as consequence, they can be neglected.

CONCLUSIONS

It has been shown that the main relation in SEA, the coupling power proportionality, is valid whatever the coupling strength is, provided that the system is limited to two subsystems and the frequency bandwidth of excitation contains the natural frequencies (only resonant modes). For more than two subsystems, the CPP can be verified as long as the couplings are weak (K i << k i is a fair condition) and the modes are resonant. For more than two oscillators, the indirect coupling loss factors appear when the coupling between subsystems is strong but the reciprocity relation always applies.

The frequency bandwidth of excitation is also important. Non-resonant modes (modes outside the frequency bandwidth of excitation) lead to the invalidity of the reciprocity relation and thus pull down the coupling power proportionality relation.

Figure 1 -

 1 Figure 1 -Three oscillators excited by uncorrelated random forces F i coupled by elastic couplings K i .

Figure 2 -

 2 Figure 2 -Evolution of β REF and β SEA for two oscillators when oscillator 1 is excited versus the coupling strength K 1 .

Table 2 -

 2 η 12 3.7E -14 3.7E -10 4.1E -6 0.0018 -0.0194 -0.0288 η 21 3.7E -14 3.7E -10 4.1E -6 0.0018 -0.0194 -0.0288 η 23 2.2E -15 2.2E -11 2.3E -η 13 -2E -26 -2E -18 -2E -10 0.0045 0.7060 0.9692 η 31 -2E -26 -2E -18 -2E -10 0Damping loss factor, coupling loss factor, indirect coupling loss factor values while the couplings K 1 = K 2 vary.

Figure 5 -

 5 Figure 5 -Sketch of the natural frequencies of each oscillator. The resonance frequency of oscillator 2 varies.

Figure 6 -

 6 Figure 6 -Evolution of the DLF and the CLF using inverse SEA. Bandwidth: [176; 353 Hz], weak couplings (K = 98E -3 N/m).

Table 1 -

 1 Oscillator parameters

	Type	Oscillator 1 Oscillator 2 Oscillator 3
	Mass m i (kg)	0.0039	0.0040	0.0036
	Stiffness k i (N/m)	9e3	9e3	9e3
	Damping λ i (S.I)	0.001	0.001	0.001

Table 3 -

 3 Oscillator parameters

	Type	Oscillator 1 Oscillator 2 Oscillator 3
	Mass m i (kg)	0.0039	variable	0.0036
	Stiffness k i (N/m)	9e3	9e3	9e3
	Damping λ i (S.I)	0.001	0.001	0.001

thibault.lafont@cpe.fr

alain.le-bot@ec-lyon.fr

nicolas.totaro@insa-lyon.fr

Inter-noise 2014

ACKNOWLEDGEMENTS

This work was supported by the Labex CeLyA of Universite de Lyon, operated by the French National Research Agency (ANR-10-LABX-0060/ ANR-11-IDEX-0007).


Marc Brysbaert
email: marc.brysbaert@rhul.ac.uk

Tatjana Nazir

VISUAL CONSTRAINTS IN WRITTEN WORD RECOGNITION: EVIDENCE FROM THE OPTIMAL VIEWING POSITION EFFECT

published or not. The documents may come

THE DROP OF VISUAL ACUITY OUTSIDE THE FIXATION LOCATION

The most important variable that limits visual word recognition is the steep drop of visual acuity outside the centre of fixation. Even at an eccentricity of 1 degree, there is already a reduction in visual acuity to about 60% of maximum [START_REF] Wertheim | Uber die indirekte Sehschärfe[END_REF]. This makes that humans find it difficult to recognise words presented a few letter positions to the left or to the right of the fixation location (unless these words are large enough). In such situations, participants have a strong tendency to move their eyes so that the stimulus word becomes fixated.

Starting from the observation that in reading participants mainly fixate words between the beginning and the middle of the word, O' Regan (1981) wondered whether the drop of acuity outside the fixation location not only had implications for the processing of parafoveally presented words (i.e., words presented a few letter positions away from the fixation location) but also for the processing of foveally presented words. His reasoning was that if the drop of visual acuity is important enough, it should be easier to recognise words after fixation on the middle letter than after fixation on the first or the last letter. Central fixation makes maximally use of the high acuity region around the fixation location, whereas fixation on the first letter makes the last letters fall more than 1 degree away from the fixation location (under usual reading conditions there are some 3 to 4 letters per degree of visual angle). The same is true for fixations on the last letter: They make the first letters fall in parafoveal vision.

To test the idea, O'Regan, Lévy-Schoen, [START_REF] O'regan | Convenient fixation location within isolated words of different length and structure[END_REF] systematically manipulated the participants' initial fixation location within a word by displaying words in such a way that they were shifted horizontally relative to an imposed fixation location. More specifically, participants had to fixate a gap between two vertically aligned fixation lines placed just above and below the horizontal position where the words were presented. Then, a word appeared shifted so that a different letter position fell between the two fixation lines (see Figure 1). An impressive series of experiments has established a consistent pattern of results due to the fixation position manipulation. This pattern is present in word naming, lexical decision, and perceptual identification. In addition, it has been observed in many different languages (French, Dutch, Hebrew, Arabic, Hebrew, Japanese). Figure 2 shows this pattern, which has been called the Optimal Viewing Position (OVP) effect, for words of 5 and 7 letters in three different tasks: Word naming, lexical decision, and perceptual identification. In all tasks, word identification was best when the words were fixated between the beginning and the middle, and performance dropped when participants were forced to fixate on the extreme letters of the words. The processing cost was larger for fixations on the end letters than for fixations on the beginning letters. To directly test the idea that the drop of visual acuity is equally important for foveally presented and parafoveally presented words, [START_REF] Brysbaert | The right visual field advantage and the optimal viewing position effect: On the relation between foveal and parafoveal word recognition[END_REF] investigated perceptual identification for 5-letter words presented so that the observers looked either four character spaces in front of the word, two character spaces in front of the word, on the first letter of the word, on the middle letter of the word, on the last letter of the word, two character spaces after the word, or four character spaces after the word. Presentation duration of the words varied from 14 ms to 70 ms. Figure 3 shows the results. As can be seen, there was no discontinuity between the performance to foveally and parafoveally presented words. Performance levels at all fixation positions were well captured by a Gaussian curve that was shifted slightly to the left (accounting for the fact that performance was better when words were presented in the right visual field than in the left visual field; see below).

MEASURING THE DROP OF PERCEPTIBILITY FOR EMBEDDED LETTERS

To get a more precise measure of the drop of letter perceptibility as a function of stimulus eccentricity, Nazir, O'Regan, and Jacobs (1991) inserted lower-case target letters in tachistoscopically presented strings of 8 k's (e.g. kkkkckkkk) and asked participants to identify the target letter. The strings were presented in such a way that participants either fixated on the first or the last k. Target letters could be presented at each location of the k-string. Figure 4 shows the findings of the study, as a function of fixation position (beginning vs. end) and position of the target letter. [START_REF] Nazir | On words and their letters[END_REF] observed three important effects. First, identification of the most extreme letter (i.e., the last letter when fixated at the beginning or the first letter when fixated at the end) was better than identification of the nearby inner letters. This is due to the well-known phenomenon of lateral inhibition [START_REF] Bouma | Interaction effects in parafoveal letter recognition[END_REF]: Letters are more difficult to recognise when they are embedded within other letters than when they are presented against an empty background. Second, when the most extreme letter was discarded the drop of performance could be approximated rather well with a linear regression (notice that a large part of the slopes of the normal distributions in Figure 3 can also be approximated by a linear regression line). Finally, Nazir et al. observed that the drop of perceptibility was stronger in the left visual field (when participants fixated on the last k) than in the right visual field (when participants fixated on the first k). The slope of the linear regression line was 1.8 times steeper in the left visual field than in the right visual field.

SIMULATING OVP CURVES ON THE BASIS OF LETTER PECEPTIBILITY

MEASURES

Subsequently, [START_REF] Nazir | On words and their letters[END_REF] examined whether they could predict the OVP curves obtained in perceptual identification, on the basis of the perceptibility of the individual letters and by assuming that a word is recognised only when all its constituent letters are recognised. Table 1 illustrates the reasoning. The first row shows the probability of identifying each letter when the participant fixates on the first letter. The probability of identifying the first letter when the eyes are on this letter is 1.00. The probability of identifying the second letter is .94 (a drop of .06). The probability of identifying the third letter is .88 (another drop of .06), and so on. Likewise, when the eyes are looking at the last letter (the last line of Table 1), the probability of identifying this letter is 1.00. The probability of identifying the second last letter is .89. This is a drop of .11, due to the fact that the perceptibility of letters decreases 1.8 times more rapidly as a function of eccentricity in the left visual field than in the right visual field. Chances of identifying the third last letter when the eyes are looking at the last letter, are .78 (i.e., another drop of .11) and so on.

Position of fixated letter

Probability 1) that all letters must be identified, (2) that the chances of perceiving a letter drop linearly as a function of the distance from the fixation location, and (3) that the drop of perceptibility is 1.8 times higher in the left visual field than in the right visual field.

Figure 5 shows the results of the simulation and compares them with empirical data obtained for 5-letter words. As can be seen, the simulation captures the asymmetry in the OVP curve very well, but underestimates the overall recognition probability of the words. Further simulations indicated that the model predicts a strong word length effect (poorer performance for long words than for short words), which is not observed in the empirical data of adults either (although it is present in the data of beginning readers; [START_REF] Aghababian | Developing normal reading skills: Aspects of the visual processes underlying word recognition[END_REF]. There are two reasons why the Nazir et al.'s (1991) simulation underestimated the overall word recognition probability. The first is that it did not take into account the higher perceptibility of the first and the last letter (see the rightmost data points in Figure 4). The second is that the model assumed that all letters must be recognised before a word can be identified. The latter assumption is not realistic, given that many words can be guessed on the basis of a subset of the letters that make the word (e.g., tabl-, h-us-). [START_REF] Stevens | Letter visibility and the viewing position effect in visual word recognition[END_REF] examined whether it was possible to predict the empirical OVP curves on the basis of individual letter recognition probabilities by taking into account the probabilities of recognising words on the basis of incomplete information. They were able to do so, if they used a coding scheme in which not the absolute letter positions were used (1 st , 2 nd , 3 rd , …) but relative letter positions (first, last, or middle letter). The latter observation agrees with the fact that words can be recognised rather easily when the positions of middle letters have been swapped [START_REF] Grainger | Does the huamn mnid raed wrods as a wlohe?[END_REF][START_REF] Perea | Can CANISO activate CASINO? Transposed letter similarity effects with nonadjacent letter positions[END_REF]. Ben [START_REF] Ben Boutayab | Interactions des facteurs visuels et lexicaux au cours de la reconnaissance des mots écrits[END_REF] recently also showed that it is possible to simulate empirical OVP curves on the basis of individual letter recognition probabilities and the probability of producing the target word on the basis of partial input.

ACCOUNTING FOR THE ASYMMETRY IN THE OVP EFFECT

In the previous sections, we have seen that because of the drop of visual acuity outside the fixation location and because of the existence of lateral inhibition not all letters of a word are equally visible. This makes that a word is more rapidly recognised when readers fixate on the centre letters than when readers fixate on the outer letters. Two factors are involved in the word processing quality (either accuracy or speed): (1) the perceptibility of the individual letters as a function of the fixation location, and (2) the extent to which the most visible letters isolate the target word from its competitors.

One element that still has to be accounted for, however, is the asymmetry in the OVP curve. Why are words recognised better after fixation on the first letters than after fixation on the last letters? Three factors seem to be involved

The first factor is the observation that in general the initial letters of a word are more informative about the identity of the word than the last letters. Some authors have argued that this is due to the way spoken words are recognised (for a review, see [START_REF] Shillcock | Eye-fixation behavior, lexical storage, and visual word recognition in a split processing model[END_REF]. Because the production of a spoken word takes a few hundred milliseconds (depending on the length), it is more efficient and communicatively effective to pack the maximum possible information at the beginning of the word. Then, spoken words can be recognised before the speaker has ended the pronunciation, leaving more time for other (syntax and discourse-related) processes.

The effect of the information distribution within words has been examined in a number of papers [START_REF] Brysbaert | The right visual field advantage and the optimal viewing position effect: On the relation between foveal and parafoveal word recognition[END_REF][START_REF] Farid | How initial fixation position influences visual word recognition: A comparison of French and Arabic[END_REF][START_REF] O'regan | Convenient fixation location within isolated words of different length and structure[END_REF][START_REF] Pynte | Within-word inspection strategies in continuous reading: Time course of perceptual, lexical and contextual processes[END_REF] and the results have been consistent. The information distribution has some influence, but on its own it does not reverse the asymmetry of the OVP effect. Figure 6 shows the results of [START_REF] Brysbaert | The right visual field advantage and the optimal viewing position effect: On the relation between foveal and parafoveal word recognition[END_REF].

Five-letter words were used that had a high informative beginning (the words had a chance of 84% to be correctly produced by participants given the first 3 letters, against a chance of only 9% when given the last 3 letters) or that had a high informative end (71% correct target production on the basis of the last 3 letters vs. 8% correct target production on the basis of the first 3 letters). The task was perceptual identification after tachistoscopic presentation.

Figure 6: Identification probability for five-letter words as a function of initial fixation (first, middle, last letter) and information distribution within the word (informative beginning [squares] vs. informative end).

As can be seen in Figure 6, the OVP pattern indeed changed as a function of the most informative word part, but whereas for words with a high informative beginning performance was much better after initial fixation on the first letter than after initial fixation on the last letter, no comparable word end advantage was observed for words with a high informative end. For these words, performance was equal after fixation on the first letter and fixation on the last letter.

A second factor that plays a role in the left-right asymmetry of the OVP effect is the reading direction. The OVP curve is much more symmetric for languages read from right to left (Arabic and Hebrew) than for languages read from left to right (French and Dutch). Figure 7 shows perceptual identification data for tachistoscopically presented Arabic and French words [START_REF] Farid | How initial fixation position influences visual word recognition: A comparison of French and Arabic[END_REF]. As can be seen, the OVP curve for Arabic has become symmetric; it has not turned into an advantage for fixations on the rightmost (initial) letter of the word. A similar pattern has been observed in Hebrew [START_REF] Nazir | Reading habits, perceptual learning, and recognition of printed words[END_REF]. The main reason why the reading direction influences the OVP effect probably has to do with low-level perceptual learning. [START_REF] Nazir | Traces of print along the visual pathway[END_REF][START_REF] Nazir | Reading habits, perceptual learning, and recognition of printed words[END_REF] argued that left to right text reading results in many words being recognised in the right visual field (either when they are in parafoveal vision or after fixation on the initial letters). There is evidence that repeated presentation of a visual stimulus in the same region of the visual field leads to enhanced discrimination of that stimulus at that particular region [START_REF] Nazir | Some results on the translation invariance in the human visual system[END_REF] but not at other nearby parts of the visual field. Given this location-dependent perceptual learning, words will be more easily recognised after fixation on the first letters than after fixation on the last letters, because during reading the eyes land more often on the first part of a word than on the last part of a word (see Figure 8). Notice that the perceptual learning factor, just like the information distribution factor, predicts that the OVP effect in a language read from right to left (Arabic, Hebrew) should be the mirror image of the effect observed in languages read from left to right (French, Dutch). So, one final factor must be invoked to explain why the OVP effect is more asymmetric for languages that are read from left to right than for languages read from right to left. [START_REF] Brysbaert | Interhemispheric transfer and the processing of foveally presented stimuli[END_REF][START_REF] Brysbaert | The importance of interhemispheric transfer for foveal vision: A factor that has been overlooked in theories of visual word recognition and object perception[END_REF] argued that this factor is the asymmetry of the brain for language processing. For the vast majority of people, the left cerebral hemisphere is more important for language processing than the right cerebral hemisphere. Because information in the right visual half field is projected directly onto the left cerebral hemisphere whereas information in the left visual half field requires interhemispheric transfer to reach the left cerebral hemisphere, word recognition will be slightly easier after fixation on the leftmost letter of a word than after fixation on the rightmost letter. This will be true both for languages read from left to right and for languages read from right to left (remember that the leftmost letter is the first letter of a word when the word is read from left to right, but the last letter of the word when the word is read from right to left). [START_REF] Brysbaert | Interhemispheric transfer and the processing of foveally presented stimuli[END_REF] tested the laterality hypothesis by comparing the OVP effect in participants with left hemisphere language dominance and participants with atypical language dominance1 . Participants named words of three, four, five, seven, and nine letters. Figure 9 shows the results. As can be seen in Figure 9, the word beginning advantage observed in unselected participants (the vast majority of whom are left dominant for language) did not turn into a word end advantage for participants with right hemisphere dominance, but the effect of cerebral dominance on the asymmetry of the OVP curve was significant. So, a third reason for the strong word beginning advantage in words that are read from left to right is related to the fact that fixation on the leftmost letter makes the whole word fall in the right visual half field which has direct connections to the dominant left hemisphere. This finding also implies that the fovea does not project information bilaterally to both hemispheres, as is sometimes assumed (see [START_REF] Brysbaert | The importance of interhemispheric transfer for foveal vision: A factor that has been overlooked in theories of visual word recognition and object perception[END_REF][START_REF] Lavidor | Magnetic stimulation studies of foveal vision[END_REF][START_REF] Leff | A historical review of the representation of the visual field in primary visual cortex with special reference to the neural mechanisms underlying macular sparing[END_REF][START_REF] Monaghan | Hemispheric asymmetries in the split-fovea model of semantic processing[END_REF][START_REF] Whitney | Hemiphere-specific effects in word recognition do not require hemisphere-specific modes of access[END_REF] for further discussion of this issue).

To further test the laterality hypothesis, [START_REF] Nazir | Reading habits, perceptual learning, and recognition of printed words[END_REF] examined the letter perceptibility as a function of eccentricity for Hebrew letters. Remember from Figure 4, that for French participants the drop of letter visibility as a function of letter eccentricity is 1.8 times steeper in the left visual field than in the right visual field. If perceptual learning is the only factor that contributes to this left-right asymmetry, then for Hebrew readers and Hebrew letters, the pattern should be reversed with an 1.8 times steeper drop in letter perceptibility for letters presented in the right visual field than for letters presented in the left visual field. On the other hand, if the right visual field advantage for French participants is the sum of perceptual learning and cerebral asymmetry, then the asymmetry should be much smaller for Hebrew participants, because for them perceptual learning (which favours the left visual field) and cerebral dominance (which favours the right visual field) should cancel each other out. This is exactly the pattern Nazir et al. ( 2004) obtained (see Figure 10). There was no difference between the regression lines in the left and the right visual field.

CONCLUSION

In this paper, we have reviewed the literature on visual constraints in written word processing. We have seen that not all letters are equally visible to the reader. The letter that is most visible is the letter that is fixated. The visibility of the other letters depends on (1) the distance between the letters and the fixation location, (2) whether the letters are outer or inner letters of the word, and (3) whether the letters lie to the left or to the right of the fixation location. Because of these three factors, word recognition depends on the viewing position. In languages read from left to right, the optimal viewing position is situated between the beginning and the middle of the word. This optimal viewing position is the result of an interplay of four variables: (1) the distance between the viewing position and the farthest letter, (2) the fact that the word beginning is usually more informative than the word end, (3) the fact that during reading, words have been recognised a lot of times after fixation on this letter position, and (4) the fact that stimuli in the right visual field have direct access to the left cerebral hemisphere. For languages read from right to left, the first three variables pull the optimal viewing position towards the right side of the word (which is the word beginning), but the fourth variable counteracts these forces to some extent. Therefore, the asymmetry of the OVP curve is less clear in Hebrew and Arabic than in French and Dutch.

Our review has concentrated entirely on the recognition of individual words. This raises the question to what extent the OVP phenomenon has implications for text reading, given that in this situation several words are presented on a line of text. We venture that the implications of the OVP phenomenon will be particularly strong for beginning readers, because for quite some time they read text materials word by word.

The situation is slightly more complicated for proficient readers, because they pick up information from the parafoveal word n+1 while they are still fixating on word n. This can be concluded from the fact that the reading rate slows down when letter information from the parafoveal word is denied (e.g., because the word remains masked until the eyes land on it) and also from the fact that skilled adult readers skip about one third of the English words (predominantly the short ones; Brysbaert, Drieghe, & Vitu, in press). This means that in text reading proficient readers quite often have rudimentary information about the word, in particular the word beginning, when they land on a word. This advance knowledge is likely to attenuate the OVP effect in text reading relative to isolated word recognition, as has indeed been observed by [START_REF] Vitu | Optimal landing position in reading isolated words and continuous text[END_REF]. However, it seems unlikely that the advance knowledge could nullify the visual constraints discussed in this paper. Indeed, the research group of McDonald and Shillcock recently showed that several of these constraints are needed for a good understanding of eye movement control both in normal [START_REF] Mcdonald | The implications of foveal splitting for saccade planning in reading[END_REF] and in dyslexic readers [START_REF] Kelly | Dyslexics' eye fixations may accommodate to hemispheric desynchronization[END_REF]).

Figure 1 :

 1 Figure 1: Example of how the initial fixation position is manipulated in the optimal viewing position paradigm. The participant is asked to fixate the gap between two vertically aligned lines. Words are presented in such a way that the participants initially look at different letter positions within the words.

Figure 2 :

 2 Figure 2 : OVP effect for 5-letter (squares) and 7-letter (triangles) words. Left panel: word naming; middle panel: lexical decision (data from[START_REF] Brysbaert | Interhemispheric transfer in reading[END_REF][START_REF] Brysbaert | Interhemispheric transfer and the processing of foveally presented stimuli[END_REF]. Notice that for these two panels, not all letter positions within the 7-letter words were tested. Right panel: perceptual identification (data from[START_REF] Stevens | Letter visibility and the viewing position effect in visual word recognition[END_REF].

Figure 3 :

 3 Figure 3 : The continuity of the drop of performance for foveally presented and parafoveally presented 5-letter words (data from Brysbaert et al., 1996). Five different presentation durations were used. The data for each duration fell on a Gaussian distribution that was slightly shifted to the left of the word centre.

Figure 4 :

 4 Figure 4 : Recognition probability of letters presented within a sequence of ks, either fixated on the first letter (open symbols) or on the last letter (closed symbols; Nazir et al., 1991).

Figure 5 :

 5 Figure 5 : Theoretical and empirical OVP curves (theoretical data based on the model presented in Table 1; empirical data from Nazir et al. (1991).

Figure 7 :

 7 Figure 7: The OVP effect for Arabic and French seven-letter words; perceptual identification (figure from Farid & Grainger, 1996).

Figure 8 .

 8 Figure 8. Correlation between the distribution of the landing sites of the eyes during reading and word recognition performance. Top panel. Distribution of landing positions in 5-and 9-letter Roman words, observed during reading. Bottom panel. Probability of correct responses for 5-to 9-letter words as a function of the location of the eyes on the word (from Nazir, 2000).

Figure 9 :

 9 Figure 9: Naming latencies for words of 3, 4, 5, 7, and 9 letters as a function of the initial fixation location and the cerebral dominance of the participants (figure from Brysbaert, 1994).

Figure 10 :

 10 Figure 10: Recognition probability of Hebrew letters in a homogenous string of characters, as a function of letter eccentricity and fixation location (left character of the string or right character).

of letter identification

							Probability of
							recognizing a
							chain of 5
							letters
		1	2	3	4	5
	1	1	.94	.88	.82	.76	.52
	2	.89	1	.94	.88	.82	.60
	3	.78	.89	1	.94	.88	.57
	4	.68	.78	.89	1	.94	.44
	5	.57	.68	.78	.89	1	.27

Table 1 : Probability of identifying a word under the assumptions (

 1

Assessment procedures available at the time did not allow the author to be 100% sure that participants were right hemisphere language dominant; some participants could have had a symmetric language representation. Therefore, the data of figure 9 are likely to slightly underestimate the effect due to cerebral dominance. We intend to repeat the study in the near future using brain imaging to assess the cerebral dominance(Knecht et al.,

2000).