Catherine Debiemme-Chouvy
email: catherine.debiemme-chouvy@upmc.fr

Ahmed Fakhry

Françoise Pillier

Electrosynthesis of polypyrrole nano/micro structures using an electrogenerated oriented polypyrrole nanowire array as framework

Keywords: Polypyrrole, nanostructures, nanowire, microstructure, electrochemical synthesis

The purpose of this paper is to show that it is possible to increase the diameter and the length of the nanostructures of a framework formed of oriented polypyrrole nanowires that has been prepared by a templateless electrochemical method based on the use of a pyrrole solution containing a high concentration of weak-acid anion and a low concentration of non-acidic anion. The dimensions of the initial nanowires are increased by performing an additional electrosynthesis in a 'classical' monomer solution. Depending on the polarization time of this last synthesis (a few tens of seconds), wires with various diameters, from one hundred up to several hundred nanometers, are obtained. In addition to the variation of the nanowire size, these findings confirm, as outlined in the reaction mechanism we have proposed, that the base of the nanowires is surrounded by a thin non-conductive polymer i.e. by an overoxidized polypyrrole film. Actually this paper shows a proof-of-concept. Indeed one can imagine that the second polymeric electrodeposit could be performed using an organic monomer solution, using functionalized pyrrole monomer to fabricate a biosensor having large specific area, and/or using anions which could be drugs.

Introduction

Polypyrrole (PPy) is one of the most widely used conducting polymers, because of its numerous advantages such as its biocompatibility, environmental stability or ease of preparation even under nanostructured form. Polypyrrole nanostructures are generally synthesized either by a chemical or by an electrochemical route using soft or hard templates [START_REF] Malinauskas | Conducting polymer-based nanostructurized materials: electrochemical aspects[END_REF][START_REF] Yin | Controlled Synthesis and Energy Applications of One-Dimensional Conducting Polymer Nanostructures: An Overview[END_REF][START_REF] Tran | One-Dimensional Conducting Polymer Nanostructures: Bulk Synthesis and Applications[END_REF][START_REF] Heinze | Electrochemistry of Conducting Polymers-Persistent Models and New Concepts[END_REF][START_REF] Long | Recent advances in synthesis, physical properties and applications of conducting polymer nanotubes and nanofibers[END_REF][START_REF] Lee | Highly aligned ultrahigh density arrays of conducting polymer nanorods using block copolymer templates[END_REF][START_REF] Piraux | Self-supported three-dimensionally interconnected polypyrrole nanotubes and nanowires for highly sensitive chemiresistive gas sensing[END_REF][START_REF] Li | Conducting polymer nanomaterials: electrosynthesis and applications[END_REF][START_REF] Lu | Polypyrrole micro-and nanowires synthesized by electrochemical polymerization of pyrrole in the aqueous solutions of pyrenesulfonic acid[END_REF][START_REF] Shen | Facile synthesis of polypyrrole nanospheres and their carbonized products for potential application in high-performance supercapacitors[END_REF][START_REF] Duvail | Physical properties of magnetic metallic nanowires and conjugated polymer nanowires and nanotubes[END_REF] or sacrificial oxidative templates such as MnO 2 [START_REF] Zhang | Reactive Template Synthesis of Polypyrrole Nanotubes for Fabricating Metal/Conducting Polymer Nanocomposites[END_REF][START_REF] Benhaddad | Chemical synthesis of hollow sea urchin like nanostructured polypyrrole particles through a core-shell redox mechanism using a MnO2 powder as oxidizing agent and sacrificial nanostructured template[END_REF][START_REF] Wang | Facile synthesis of hierarchical conducting polypyrrole nanostructures via a reactive template of MnO2 and their application in supercapacitors[END_REF][START_REF] Dubal | Growth of polypyrrole nanostructures through reactive templates for energy storage applications[END_REF]. For instance, PPy nanotubes can be synthesized by using a self-degraded methyl orange (MO) template method [START_REF] Yang | Facile fabrication of functional polypyrrole nanotubes via a reactive self-degraded template[END_REF][START_REF] Yang | Electrochemical synthesis of functional polypyrrole nanotubes via a self-assembly process[END_REF][START_REF] Sapurina | Polypyrrole nanotubes: The tuning of morphology and conductivity[END_REF][START_REF] Kopecka | Polypyrrole nanotubes: mechanism of formation[END_REF].

However It has also been established that polypyrrole nanostructures can also be prepared without the use of any template [START_REF] Debiemme-Chouvy | Nanostructured polypyrrole materials: Focus on templateless synthetic methods and on some applications[END_REF][START_REF] Chronakis | Conductive polypyrrole nanofibers via electrospinning: Electrical and morphological properties[END_REF][START_REF] Tran | A template-free route to polypyrrole nanofibers[END_REF][START_REF] Turco | Templateless synthesis of polypyrrole nanowires by non-static solution-surface electropolymerization[END_REF][START_REF] Tran | Toward an understanding of the formation of conducting polymer nanofibers[END_REF]. Notably In that respect, in our previous works [START_REF] Debiemme-Chouvy | Template-free one-step electrochemical formation of polypyrrole nanowire array[END_REF][START_REF] Fakhry | Templateless electrogeneration of polypyrrole nanostructures: impact of the anionic composition and pH of the monomer solution[END_REF][START_REF] Fakhry | Mechanism of formation of templateless electrogenerated polypyrrole nanostructures[END_REF], we brought to light that different polypyrrole nanostructures can be synthesized by using a one-step electrochemical synthesis, without using any template. Indeed, nanostructured polypyrrole films, which are superhydrophilic, are electrogenerated in the presence of (i) a high concentration of weak-acid anions conferring to upon the monomer solution a pH between 6 and 10, and (ii) a low concentration of non-acidic anions such as perchlorate ions.

Notably, Depending on the perchlorate concentration of a 0.15 M pyrrole solution containing 0.2 M monohydrogenophosphate, different PPy nanostructures can be synthesized. In the absence or in the presence of a very low concentration of perchlorate ions (< 10 -4 M), an ultrathin non-conductive overoxidized PPy (OPPy) film is obtained [START_REF] Debiemme-Chouvy | One-step electrochemical synthesis of a very thin overoxidized polypyrrole film[END_REF][START_REF] Fakhry | Electrochemical Characterisations of Ultra Thin Overoxidized Polypyrrole Films Obtained by One-Step Electrosynthesis[END_REF]. This finding was notably determined in particular by XPS analyses [START_REF] Debiemme-Chouvy | One-step electrochemical synthesis of a very thin overoxidized polypyrrole film[END_REF] and EIS studies [START_REF] Fakhry | Electrochemical Characterisations of Ultra Thin Overoxidized Polypyrrole Films Obtained by One-Step Electrosynthesis[END_REF]. Notice that due to its compactness, this OPPy film prevents the diffusion to the electrode surface of large redox species such as ascorbic acid and dopamine [START_REF] Debiemme-Chouvy | A very thin overoxidized polypyrrole membrane as coating for fast time response and selective H 2 O 2 amperometric sensor[END_REF] or Fe(CN) 6 4-and Ru(NH 3 ) 6 2+ [START_REF] Debiemme-Chouvy | Characterization of a very thin overoxidized polypyrrole membrane: application to H 2 O 2 determination[END_REF] avoiding their electro-oxidation. It is not the case for On the contrary, small molecules such as H 2 O 2 and H 2 O can diffuse across this OPPy film, and therefore be oxidized at the electrode surface [START_REF] Debiemme-Chouvy | A very thin overoxidized polypyrrole membrane as coating for fast time response and selective H 2 O 2 amperometric sensor[END_REF][START_REF] Debiemme-Chouvy | Characterization of a very thin overoxidized polypyrrole membrane: application to H 2 O 2 determination[END_REF]. In the presence of a higher concentration of perchlorate ions (> 10 -4 M), an oriented nanowire (NW) array or a network of more or less interconnected nanofibers is formed [START_REF] Fakhry | Templateless electrogeneration of polypyrrole nanostructures: impact of the anionic composition and pH of the monomer solution[END_REF].

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 3
The oxidation of pyrrole (Py) leads to the formation of polypyrrole under its oxidized form, doped with anions (A -), according to the following reaction [START_REF] Genies | Spectroelectrochemical study of polypyrrole films[END_REF][START_REF] Qiu | Electrochemically initiated chain polymerization of pyrrole in aqueous-media[END_REF]:

(

where γ stands for the doping level of the polymer, it is generally in the range of 0.25 to 0.33 [START_REF] Diaz | Electrochemical preparation and characterization of conducting polymers[END_REF][START_REF] Salmon | Chemical modification of conducting polypyrrole films[END_REF].

In the presence of weak-acid anions, the protons released during Py oxidation (reaction (1)) are captured by these anions:

(

The mechanism that we have previously proposed in order to explain the formation of a nanostructured polypyrrole film is summarized in Figure 1. It is based on the variation of the interfacial concentration of anions. Indeed, during pyrrole oxidation protons are released during pyrrole oxidation, and collected by the weak-acid anions present in the solution (reaction (2)), which results in a drastic decrease, or elimination, of anions at the electrode/solution interface. As pyrrole oxidation requires the presence of anions (reaction (1)), which are no longer available, this reaction cannot occur anymore. Instead, water oxidation takes place, leading to the formation of hydroxyl radicals. These radicals can either react with the already formed polypyrrole film, resulting in its overoxidation [START_REF] Debiemme-Chouvy | An insight into the overoxidation of polypyrrole materials[END_REF], or with themselves, leading to the formation of H 2 O 2 molecules. These molecules are subsequently oxidized into O 2 molecules that form nanobubbles inside the polymer, protecting it against the hydroxyl radical action and therefore preventing locally its overoxidation allowing the conservation of some conductive zones (step 2 in Fig. 1). After bubbles evolution, the electrooxidation of the monomers takes place at these zones, leading to the formation of nanorods/nanowires (step in Figure 1). Obviously, the diameter of these PPy nanostructures depends on the size of the O 2 bubbles. Notice that this process differs from the

- - ) ( n - e 2) - n ) ((2 H 1) - 2(n ] A n , [(Py) A n Py n γ + + + γ → γ + + + γn AH H A - → + + M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 4
one involving gas bubbles as template for PPy microstructure electrosynthesis [START_REF] Qu | Preparation of polypyrrole microstructures by direct electrochemical oxidation of pyrrole in an aqueous solution of camphorsulfonic acid[END_REF][START_REF] Mazur | Polymerization at the Gas/Solution interface: Preparation of polymer microstructures with gas bubbles as templates[END_REF][START_REF] Parakhonskiy | Polypyrrole Microcontainers: Electrochemical Synthesis and Characterization[END_REF].

Therefore, the nature of the anions present in the monomer solution and the release of protons during Py electropolymerization are the two main parameters involved in polypyrrole nanostructure formation. Under potentiostatic conditions, using a Pt anode, the diameter of the PPy nanowires is about 80 nm and their length depends on the electrode polarization time [START_REF] Debiemme-Chouvy | Template-free one-step electrochemical formation of polypyrrole nanowire array[END_REF]. It has been shown that PPy nanostructures are also electrogenerated in the presence of a low monomer concentration and a high non-acidic anion concentration [START_REF] Fakhry | Templateless electrogeneration of polypyrrole nanostructures: impact of the anionic composition and pH of the monomer solution[END_REF].

The oriented PPy nanowire array could be used as framework for the growth of larger and longer nano/micro structures. Therefore, a second electrosynthesis could be done, using for example functionalized monomer in order to prepare a biosensor [START_REF] Baur | Immobilization of biotinylated biomolecules onto electropolymerized poly(pyrrole-nitrilotriacetic acid)-Cu2+ film[END_REF][START_REF] Baur | Label-Free Femtomolar Detection of Target DNA by Impedimetric DNA Sensor Based on Poly(pyrrolenitrilotriacetic acid) Film[END_REF][START_REF] Giroud | Impedimetric Immunosensor Based on a Polypyrrole-Antibiotic Model Film for the Label-Free Picomolar Detection of Ciprofloxacin[END_REF][START_REF] Gondran | Electrogenerated poly(pyrrole-Iactosyl) and poly(pyrrole-3 '-sialyllactosyl) interfaces: toward the impedimetric detection of lectins[END_REF][START_REF] Haddour | Electrogeneration of a poly(pyrrole)-NTA chelator film for a reversible oriented immobilization of histidine-tagged proteins[END_REF][START_REF] Kazane | Highly Sensitive Bisphenol-A Electrochemical Aptasensor Based on Poly(Pyrrole-Nitrilotriacetic Acid)-Aptamer[END_REF][START_REF] Xu | Label-free impedimetric thrombin sensor based on poly(pyrrole-nitrilotriacetic acid)aptamer film[END_REF], or using organic solvent containing anions which could be drugs [START_REF] Guiseppi-Elie | Electroconductive hydrogels: Synthesis, characterization and biomedical applications[END_REF][START_REF] Novak | Overoxidation of polypyrrole in propylene carbonate -An in situ FTIR study[END_REF][START_REF] Samanta | Electroresponsive nanoparticles for drug delivery on demand[END_REF][START_REF] Sirivisoot | Electrically controlled drug release from nanostructured polypyrrole coated on titanium[END_REF].

The aim of the present work is to show that it is possible to vary the diameter and the length of oriented polypyrrole nanowires by performing an additional electrosynthesis in a classical pyrrole solution. Moreover, it allows to confirm provides confirmation that the nanowire base is surrounded by an overoxidized polypyrrole (non-conductive polymer) layer. These experiments also allow us to determine whether or not the polypyrrole nanowires are conductive over their entire surface (top and sides). Therefore, the strategy developed in the present work involves two steps. The first one requires is the electrogeneration of oriented nanowires, which requires the use of a monomer solution containing a high concentration of weak-acid anions and a low concentration of perchlorates. The second step is based on implies an anodic polarization in a classical monomer solution, without any weak-acid anions.

Experimental

The polypyrrole films were synthesized using preliminarily distilled pyrrole. All the solutions were prepared with bi-distilled water. Py, K 2 HPO 4 , KCH 3 COO, K 2 CO 3 and LiClO 4 were purchased from Aldrich.

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 5

The electrochemical experiments were performed in a classical three-electrode electrochemical cell. A platinum foil was used as counter electrode and a saturated calomel electrode (SCE) was used as the reference electrode. A double junction was used in order to avoid chloride diffusion into the Py solution. The working electrode was a Pt electrode (S = 0.07 cm 2 ) for electrochemical studies and Au/mica substrate for SEM observations. The preparation of the substrates was the following: a thin gold film (∼ 80 nm) was deposited under low pressure (10 -4 Pa) by thermal evaporation on a mica substrate.

All the electrosyntheses were performed under potentiostatic conditions, in 0.15 M Py + 0.2 M K 2 HPO 4 + 10 -3 M LiClO 4 for the first synthesis and in 0.15 M Py + 0.2 M LiClO 4 for the second one, during given times named t 1 and t 2, respectively. The pH of the first monomer solution is 8.9.

The superhydrophilic character of the PPy films was checked by a drop test experiment: a small pure water droplet is deposited onto the film and the spreading of the drop is analyzed.

When the film is nanostructured the drop spreads out whereas it does not for classical cauliflower-like films.

As far as the electropolymerization of Py is concerned, an Autolab PGSTAT30 potentiostat (Ecochemie) controlled with the GPES software was employed. The film morphology was examined under a field emission gun scanning electron microscope (FEG-SEM), Ultra55

Zeiss, operating at 5 kV.

Results and discussion

Firstly, the framework composed of oriented PPy nanowires was synthesized by performing an electropolymerization of pyrrole monomers under potentiostatic conditions at 0.75 V/SCE in a solution composed of 0.15 M pyrrole, 0.2 M K 2 HPO 4 and 10 -3 M LiClO 4 (see the SEM micrograph in Fig. 1). The average diameter of the nanowires is about 80 nm. Notice that the framework can also be obtained using acetate or carbonate instead of Some of these polypyrrole films have been observed by SEM. In Figure 4 are reported micrographs obtained for a Au/mica substrate first polarized during 600 s and then only a part of its surface has undergone a second polarization of 50 seconds in the 'classical' Py solution.

M A N U S C R I P T A C C E P T E D
Micrograph A shows the film after the first polarization whereas micrographs B-D show the film after the second polarization. Notice that micrographs A and B were done at the same magnitude. From these micrographs, it is clear that the growth of the polymer occurs on the whole surface of the nanowires, since their diameter increases from 80 nm to 200 nm and their length increases from 500 nm [START_REF] Debiemme-Chouvy | Template-free one-step electrochemical formation of polypyrrole nanowire array[END_REF] up to 1500 nm. Besides, the SEM micrographs presented in Figure 5 show different kinds of (nano)structures depending on the second synthesis duration, the first synthesis duration being 200 s. When the second polarization duration increases, the polypyrrole nanowire diameter increases too. Finally, for 300 seconds of polymerization, the PPy film has a cauliflower-like structure (see micrographs E and F in Fig. 5) because during the electrode polarization the diameter of the nanowires has so increased that their coalescence has occurred.

The diameter and the length of the polypyrrole nanostructures obtained after the second synthesis versus the polymerization duration are plotted in Figure 6. The nanowire diameter increases with the second polarization time, leading to a decrease of the space between the nanowires until they coalesce and form a 2D film that is no more superhydrophilic, confirming that the PPy film is no longer nanostructured. Before coalescence, the growth rate in terms of fiber length is about 17 nm s -1 , which is almost 20 times faster than the rate determined with the Py solution used for the framework synthesis (first synthesis) [START_REF] Debiemme-Chouvy | Template-free one-step electrochemical formation of polypyrrole nanowire array[END_REF]. This finding could be explained by the fact that the Py electropolymerization is limited by the concentration of the anions present at the electrode/solution interface [START_REF] Li | Effect of anion concentration on the kinetics of electrochemical polymerization of pyrrole[END_REF]. are plotted in Fig. 7A. As it can be noticed from this figure, for a given t 2 , the relationship between Q 1 and Q 2 is linear and does not pass through the origin:

Q 2 = α + β Q 1 (3)
Whatever t 1 is, the first synthesis leads to PPy nanowire arrays having the same NW density and the same NW diameter, as these parameters only depend on the nature of the electrode, the applied potential, and the Py solution composition. Therefore, the variation of t 1 i.e. Q 1 only leads to the variation of the PPy nanowire length [START_REF] Debiemme-Chouvy | Template-free one-step electrochemical formation of polypyrrole nanowire array[END_REF]. As the relationship between the charge passed during the second synthesis and the one passed during the first synthesis (which determines the length of the wires) is linear, one can conclude that the Py polymerization takes place on the entire surface of the nanowires and not only on their top. In this latter case, Q 2 should be independent of the wire length i.e. of Q 1 . This conclusion is in good agreement with the SEM observations (Figs. [START_REF] Heinze | Electrochemistry of Conducting Polymers-Persistent Models and New Concepts[END_REF][START_REF] Long | Recent advances in synthesis, physical properties and applications of conducting polymer nanotubes and nanofibers[END_REF][START_REF] Lee | Highly aligned ultrahigh density arrays of conducting polymer nanorods using block copolymer templates[END_REF].

α and β parameters of equation ( 3) versus the polarization time of the second synthesis are depicted in Figures 7B and7C, respectively. These relationships are linear. Q 2 corresponds to Py oxidation that leads to the generation of PPy at a place that can be divided into three zones, named a, b, and c in Figure 8.

Q 2 = Q a + Q b + Q c ( 4
)
As it can be noticed from Figure 8, zone a corresponds to the enlargement of the nanowires.

Therefore, Q a is function of Q 1 . Zones b and c correspond to the elongation of the nanowires and the enlargement of the nanowires formed during the second synthesis, respectively.

Therefore both Q b and Q c are independent from Q 1 , which sets the length of the nanowire. α is proportional to t 2 as evidenced by Fig. 7B, in good agreement with Fig. 6.

Consequently, taking into account that α = Q b + Q c and β = a t 2 , the combination of equations ( 3) and (4) gives

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Q 2 = Q b + Q c + a t 2 Q 1 (5)
All these findings allow us to confirm that the PPy nanowires of the framework (first synthesis) are conductive on their entire surface i.e. on their top and their sides, as it is possible to deposit another polypyrrole film all around the nanostructures whose diameter increases with the polarization time of the second synthesis (Fig. 6). Moreover, these findings confirm that the layer which surrounds the nanowire base is non-conductive i.e. it is an overoxidized PPy film, as shown in the mechanism proposed in our previous work regarding the formation of the different polypyrrole nanostructures (see Fig. 1).

Conclusions

It is possible to use oriented polypyrrole nanowires obtained by a one-step templateless electrochemical method as framework to perform a second synthesis in a pyrrole solution that does not contain a high concentration of weak-acid anions. Depending on the polarization duration of this synthesis, the diameter of the nanowires varies from a few tens of nanometers up to several hundred nanometers, and after a threshold of polarization time cauliflower-like structures are obtained. These findings are important because they allow us to confirm the global mechanism we proposed to achieve the electrogeneration of PPy nanostructure without the use of any template, just by using a monomer solution containing a high concentration of weak-acidic anions having pKa > 6. Finally, this paper shows a proof-of-concept, indeed one can imagine that the second electrodeposit could be performed using functionalized pyrrole monomer to fabricate electrochemical biosensors with large specific area, for example or using an organic solvent, and/or using anions which could be drugs.

Figures

  Figures 2A and 2B (curves (a)) show the anodic current versus time responses for the first and

  Finally, a series of PPy framework was prepared by varying the polarization durations (t 1 ), leading to different anodic charges (Q 1 ). A second polarization, lasting a given time (t 2 = 10, 15, 20, 30, 40 or 50 seconds), was then M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 8 performed in the 'classical' Py solution (solution containing only perchlorates as anions). The anodic charges after the second synthesis (Q 2 ) vs. anodic charges after the first synthesis (Q 1 )

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. General mechanism of PPy nanowire electrogeneration. Left: Drawing showing the evolution of the polymer deposit with the anodic polarization time, in Py aqueous solution containing a high concentration of K 2 HPO 4 and a low concentration of LiClO 4 . Right: SEM micrograph (side view, 60°) of PPy nanowires electrosynthesized at 0.75 V/SCE for 200 s in 0.15 M Py + 0.2 M K 2 HPO 4 + 10 -3 M LiClO 4 aqueous solution. Steps 1, 3, 4: Py oxidation ; step 2 : water oxidation leading to OH and O 2 formation. O 2 locally protect the PPy film against the action of OH which overoxidizes PPy. Steps 1, 2, 3 last around a few seconds.

Figure 4 .Figure 5 .Figure 6 . 1 Figure 7 . 21 Figure 8 .

 45617218 Figure 4. SEM micrographs of PPy. A) after a first synthesis at 0.75 V/SCE for 600 s in 0.15 M Py + 0.2 M K 2 HPO 4 + 10 -3 M LiClO 4 aqueous solution. B-D) after a second synthesis at 0.75 V/SCE for 50 s in 0.15 M Py + 0.2 M LiClO 4 aqueous solution. A,B,C top view, D side view (60°). Scale bar: 200 nm (A), (B), (D) ; 100 nm (C).

  COO -, we have obtained the same results i.e. nanostructured PPy films (see Table1). Then, another polypyrrole deposit was performed onto these nanostructures. This second electrosynthesis was conducted in a monomer solution containing only perchlorates as anions, solution which generally leads to the formation of a PPy film with cauliflower-like structure.

	ACCEPTED MANUSCRIPT monohydrogenophosphate. Indeed by replacing HPO 4 2-by CO 3	2-or CH 3

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT

Nanostructured film ; (2) Overoxidized PPy film ; (3) pH adjusted with KOH


Built-In Self-Test Structure (BIST) for Resistive RAMs Characterization: Application to Bipolar OxRRAMs

Hassen AZIZA, Marc BOQUET, Mathieu MOREAU and J-Michel PORTAL IM2NP-UMR CNRS 6242, Aix-Marseille University, France, hassen.aziza@im2np.fr

Problem Formulation: Resistive Random Access Memory (ReRAM) is a form of nonvolatile storage that operates by changing the resistance of a specially formulated solid dielectric material [START_REF] Gibbons | Switching properties of thin Nio films[END_REF]. Among ReRAMs, Oxide-based Resistive RAMs (so-called OxRRAM) are promising candidates due their compatibility with CMOS processes and high ON/OFF resistance ratio. Common problems with OxRRAM are related to high variability in operating conditions and low yield. OxRRAM variability mainly impact ON/OFF resistance ratio. This ratio is a key parameter to determine the overall performance of an OxRRAM memory. In this context, the presented built-in structure allows collecting statistical data related to the OxRRAM memory array (ON/OFF resistance distributions) for reliability assessment of the technology.

Built-In Self-Test (BIST) structure: Fig. 1a presents the elementary array used for simulation which is constituted by a 3×3 1T/1R cell matrix, a row decoder, a column decoder and a sense amplifier for the read operation. The OxRRAM compact model used to build the memory array satisfactorily matches quasi-static and dynamic experimental data measured on actual HfO 2 -based devices (Fig. 1b). Notice that the OxRRAM model allows a variability analysis based on OxRRAM card model parameters variation. The card model parameter variations are chosen to feet experimental data [START_REF] Aziza | Evaluation of OxRAM cell variability impact on memory performances through electrical simulations[END_REF]. Fig. 2 presents the single-ended sense amplifier (solid line). The circuit working principle is quite simple. During a READ operation, the OxRRAM cell is biased throw the row decoder (with V read > 0 and WL high). The current cell value I cell is generated according to the memory state (RON≈7kΩ after a SET operation and ROFF≈25 kΩ after a RESET operation).

Simulation results:

To validate the BIST structure, a variability analysis is conducted through 500 Monte Carlo simulations. The elementary matrix presented in Fig. 1, embedding the BIST structure, is considered for simulations. As a result of cell variability, circuit performance exhibits much wider variability. In Fig. 3a, RON and ROFF distributions are plotted (RON/ROFF variability being correlated with actual silicon results). In Fig. 3b, V IN SET and V IN RESET distributions are also plotted. These last distributions are similar to RON and ROFF distributions. This trend is confirmed by the correlation curve presented in Fig. 4. Therefore, the modified sense amplifier structure can be used as a powerful tool to track any resistance variations but also to characterize the memory array variability.

To summarize, a built in self-test structure is presented for Resistive RAM characterization and variability evaluation. The area overhead introduced by structure is relatively low as the structure is integrated in the sense amplifiers. The normal mode of operation of the memory is preserved. Besides, extracted values are given in a digital data format, so the extraction process does not required any analog pin on the tester, making it fully digital tester compliant or easily observable via the random logic.

Fig. 1 (Fig. 2

 12 Fig. 1 (a) OxRRAM memory array (a) I-V characteristic measured on HfO2-based devices and corresponding simulation using the OxRRAM model & SET voltage as a function of the programming ramp

  The comparator input V IN is directly proportional to I cell and therefore to the OxRRAM resistance (two distinct values are available for V IN : V IN SET for RON and V IN RESET for ROFF). So that the sense amplifier operates properly, on the one hand the difference between V IN SET and V IN RESET must be the highest and on the other hand V REF has to be set exactly between V IN SET and V IN RESET values. At a circuit level, V IN is the parameter to consider in terms of memory functionality. To extract V IN value, a variable voltage reference source and a multiplexer are incorporated in the sensing circuit (dotted part of the circuit) and the READ operation is modified as follow:• V REF increases step by step from 0 to V dd (V REF increase is controlled by a shift register),• V IN value is detected when the sense amplifier output switches (the shifting process stops when V REF > V IN , i.e Rd_REG signal becomes active), • V IN is available at the circuit output in a numerical value when BIST_EN signal is high.


Tien-Duc Cao
email: tien-duc.cao@inria.fr

Ioana Manolescu
email: ioana.manolescu@inria.fr

Xavier Tannier
email: xavier.tannier@sorbonne-universite.fr

Searching for Truth in a Database of Statistics

The proliferation of falsehood and misinformation, in particular through the Web, has lead to increasing energy being invested into journalistic fact-checking. Fact-checking journalists typically check the accuracy of a claim against some trusted data source. Statistic databases such as those compiled by state agencies are often used as trusted data sources, as they contain valuable, high-quality information. However, their usability is limited when they are shared in a format such as HTML or spreadsheets: this makes it hard to find the most relevant dataset for checking a specific claim, or to quickly extract from a dataset the best answer to a given query.

We present a novel algorithm enabling the exploitation of such statistic tables, by (i) identifying the statistic datasets most relevant for a given fact-checking query, and (ii) extracting from each dataset the best specific (precise) query answer it may contain. We have implemented our approach and experimented on the complete corpus of statistics obtained from INSEE, the French national statistic institute. Our experiments and comparisons demonstrate the effectiveness of our proposed method.

INTRODUCTION

The media industry has taken good notice of the value promises of big data. As more and more important and significant data is available in digital form, journalism is undergoing a transformation whereas technical skills for working with such data are increasingly needed and also present in newsrooms. In particular, the areas of data journalism and journalistic fact checking stand to benefit most from efficient tools for exploiting digital data.

Statistics databases produced by governments or other large organizations, whether commercial or administrative ones, are of particular interest to us. Such databases are built by dedicated personnel at a high cost, and consolidated carefully out of multiple inputs; the resulting data is typically of high value. Another important quality of such data is that it is often shared as open data. Example of such government open data portals http://data.gov (US), http://data.gov.uk (UK), http://data.gouv.fr and http://insee.fr (France), http://wsl.iiitb.ac.in/sandesh-web/sandesh/index (India) etc.

While the data is open, it is often not linked, that is, it is not organized in RDF graphs, as recommended by the Linked Open Data best practice for sharing and publishing data on the Web [START_REF] W3c | Best Practices for Publishing Linked Data[END_REF]. Instead, such data often consists of HTML or Excel tables containing numeric data, links to which appear in or next to text descriptions of their contents. This prevents the efficient exploitation of the data through automated tools, capable for instance to answer queries such as "find the unemployment rate in my region in 2015".

In a prior work [START_REF] Cao | Extracting Linked Data from Statistic Spreadsheets[END_REF], we have devised an approach to extract from high-quality, statistic Open Data in the "tables + text description" frequently used nowadays, Linked Open Data in RDF format; this is a first step toward addressing the above issues. Subsequently, we applied our approach to the complete set of statistics published by INSEE, a leading French national statistics institute, and republish the resulting RDF Open Data (together with the crawling code and the extraction code 1 ). While our approach is tailored to some extent toward French, its core elements are easily applicable to another setting, in particular for statistic databases using English, as the latter is very well-supported by text processing tools.

In this work, we introduce novel algorithms for searching for answers to keyword queries in a database of statistics, organized in RDF graphs such as those we produced. First, we describe a dataset search algorithm, which given a set of user keywords, identifies the datasets (statistic table and surrounding presentations) most likely to be useful for answering the query. Second, we devised an answer search algorithm which, building on the above algorithm, attempts to answer queries, such as "unemployment rate in Paris in 2016", with values extracted from the statistics dataset, together with a contextualization quickly enabling the user to visually check the correctness of the extraction and the result relevance. In some cases, there is no single number known in the database, but several semantically close ones. In such cases, our algorithm returns the set of numbers, again together with context enabling its interpretation.

We have experimentally evaluated the efficiency and the effectiveness of our algorithms, and demonstrate their practical interest to facilitate fact-checking work. In particular, while political debates are broadcast live on radio or TV, fact-checkers can use such tools to quickly locate reference numbers which may help them publish live fact-checking material.

In the sequel, Section 2 outlines the statistic data sources we consider, their organization after our extraction [START_REF] Cao | Extracting Linked Data from Statistic Spreadsheets[END_REF], and our system architecture. Section 3 defines the search problem we address, and describes our search algorithms. Section 4 presents our experimental evaluation. We discuss related work in Section 5, then conclude. 1. The outline, when present, is a short paragraph on the Web page from where D i can be downloaded. The outline extends the title with more details about the nature of the statistic numbers, the interpretation of each dimension, methodological details of how numbers were aggregated etc.

INPUT DATA AND ARCHITECTURE

A dataset consists of header cells and data cells. Data cells and header cells each have attributes indicating their position (column and row) and their value. For instance, in Figure 1, "Belgium", "Bulgaria" etc. up to "France", "Oct-2016" to "Oct-2017", and the two fused cells above them, are header cells, while the other are data cells. Each data cell has a closest header cell on its column, and another one on its row; these capture the dimension values corresponding to a data cell.

To enable linking the results with existing (RDF) datasets and ontologies, we extract the table contents into an RDF graph. We create an RDF class for each entity type, and assign an URI to each entity instance, e.g., dataset, table, cell, outline etc. Each relationship is similarly represented by a resource described by the entity instances participating to the relationship, together with their respective roles. System architecture A scraper collects the Web pages publicly accessible from a statistic institute Web site; Excel and HTML tables are identified, traversed, and converted into RDF graphs as explained above. The resulting RDF data is stored in the Apache Jena TDB server 2 , and used to answer keyword queries as we explain next.

SEARCH PROBLEM AND ALGORITHM

Given a keyword-based query, we focus on returning a ranked list of candidate answers, ordered in the decreasing likelihood that they contain (or can be used to compute) the query result. A relevant candidate answer can be a data cell, a data row, column or even an entire dataset. For example, consider the query "youth unemployment in France in August 2017". An Eurostat dataset3 is a good candidate answer to this query, since, as shown in Figure 1, it contains one data cell, at the intersection of the France row with the Aug-2017 column. Now, if one changes the query to ask for "youth unemployment in France in 2017", no single data cell can be returned; instead, all the cells on the France row qualify. Finally, a dataset containing 2017 French unemployment statistics over the general population (not just youth) meets some of the search criteria (2017, France, unemployment) and thus may deserve to appear in the ranked list of results, depending on the availability of better results.

This task requires the development of specific novel methods, borrowing ideas from traditional IR, but following a new methodology. This is because our task is very specific: we are searching for information not within text, but within tables, which moreover are not flat, first normal form database relations (for which many keyword search algorithms have been proposed since [START_REF] Hristidis | DISCOVER: Keyword Search in Relational Databases[END_REF]), but partially nested tables, in particular due to the hierarchical nature of the headers, as we explained previously. While most of the reasoning performed by our algorithm follows the two-dimensional layout of data in columns and tables, bringing the data in RDF: (i) puts a set of interesting, high-value data sources within reach of developers and (ii) allows us to query across nested headers using regular path queries expressed in SPARQL (as we explain in Section 3.5).

We describe our algorithms for finding such answers below.

Dataset search

The first problem we consider is: given a keyword query Q consisting of a set of keywords u 1 , u 2 , . . ., u m and an integer k, find the k datasets most relevant for the query (we explain how we evaluate this relevance below). We view each dataset as a table containing a title, possibly a comment, a set of header cells (row header cells and column header cells) and a set of data cells, the latter containing numeric data 4 . At query time, we transform the query Q into a set of of keywords W = w 1 , w 2 , . . . , w n using the method described in Section 3.2. Offline, this method is also used to transform each dataset's text to words and we compute the score of each word with respect to a dataset, as described in Section 3.3. Then, based on the word-dataset score and W , we estimate datasets' relevance to the query as we explain in Section 3.4.

Text processing

Given a text t (appearing in a title, comment, or header cell of the dataset, or the text consisting of the set of words in the query Q), we convert it into a set of words using the following process:

• First, t is tokenized (separated into distinct words) using the KEA5 tokenizers. Subsequently, each multi-word French location found in t that is listed in Geonames 6 , is put together in a single token. • Each token (word) is converted to lowercase, stop words are removed, as well as French accents which complicate matching. • Each word is mapped into a word2vec vector [START_REF] Mikolov | Distributed Representations of Words and Phrases and their Compositionality[END_REF], using the gensim [START_REF] Radim | Software Framework for Topic Modelling with Large Corpora[END_REF] tool. Bigrams and trigrams are considered following [START_REF] Mikolov | Distributed Representations of Words and Phrases and their Compositionality[END_REF]. We had trained the word2vec model on a generaldomain French news web page corpus.

Word-dataset score

For each dataset extracted from the statistic database, we compute a score characterizing its semantic content. A first observation is that datasets should be returned not only when they contain the exact words present in the query, but also if they contain very closely related ones. For example, a dataset titled "Duration of marriage" could be a good match for the query "Average time between marriage and divorce" because of the similarity between "duration" and "time". To this effect, we rely on word2vec [START_REF] Mikolov | Distributed Representations of Words and Phrases and their Compositionality[END_REF] which provides similar words for any word in its vocabulary: if a word w appears in a dataset D, and w is similar to w ′ , we consider w ′ also appears in D.

The score score (w ) of a dataset D w.r.t the query word w is 1 if w appears in D. .

If w does not appear in D:

• If there exists a word w ′ , from the list of top-50 similar words of w according to word2vec, which appears in D, then score (w ) is the similarity between w and w ′ . If there are several such w ′ , we consider the one most similar to w. • If w is the name of a Geonames place we can't apply the above scoring approach because "comparable" places (e.g., cities such as Paris and London) will have high similarity in the word2vec space. As the result, when user asks for "unemployment rate Paris", the data of London might be returned instead of Paris's. Let p be the number of places that Geonames' hierarchy API 7 returns for w (p is determined by Geonames and depends on w). For instance, when querying the API with Paris, we obtain the list Île-de-France, France, Europe. Let w ′ i be the place at position i, 1 ≤ i ≤ p in this list of returned places, such that w ′ i appears in D. Then, we assign to D a score for w equal to (p + 1 -i)/(p + 1), that is, the most similar place according to Geonames has rank p/(p + 1), and the least similar has the rank 1/(p + 1). If D contains several of the places from the w's hierarchy, we assign to D a score for w corresponding to the highest-ranked such place.

• 0 otherwise.

Based on the notion of word similarity defined above, we will write w ≺ W to denote that the word w from dataset D either belongs to the query set W , or is close to a word in W . Observe that, by definition, for any w ≺ , we have score (w ) > 0.

We also keep track of the location(s) (title, header and/or comment) in which a word appears in a dataset; this information will be used when answering queries, as described in Section 3.4. In summary, for each dataset D and word w ∈ D such that w ≺ W , we compute and store tuples of the form: These tuples are encoded in JSON and stored in a set of files; each file contains the scores for one word (or bigram) w, and all the datasets.

Relevance score function

Content-based relevance score function. This function, denoted д 1 (D,W ), quantifies the interest of dataset D for the word set W ; it is computed based on the tuples (w, score (w ), location(w, D), D) where w ≺ W .

We experimented with many score functions that give high ranking to datasets that have many matching keywords (see details in section 4.2.2). These functions are monotonous in the score of D with respect to each individual word w. This enables us to apply Fagin's threshold algorithm [START_REF] Fagin | Optimal Aggregation Algorithms for Middleware[END_REF] to efficiently determine the k datasets having the highest д 1 score for the query W .

Location-aware score components. The location -title (T), row or column headers (HR or HC), or comments (C) -where a keyword occurs in a dataset can also be used to assess the dataset relevance. 7 http://www.geonames.org/export/place-hierarchy.html For example, a keyword appearing in the title often signals a dataset more relevant for the search than if the keyword appears in a comment. We exploit this observation to pre-filter the datasets for which we compute exact scores, as follows.

We run the TA algorithm using the score function д 1 to select r × k datasets, where r is an integer larger than 1. For each dataset thus retrieved, we compute a second, refined score function д 2 (see below), which also accounts for the locations in which keywords appear in the datasets; the answer to the query will consists of the top-k datasets according to д 2 .

The second score function д 2 (D,W ) is computed as follows. Let w ′ be a word appearing at a location loc ∈ {T, HR, HC, C} such that w ′ ≺ W . We denote by w ′ loc, D (or just w ′ loc when D is clear from the context) the existence of one or several located occurrence of w ′ in D in loc. Thus, for instance, if "youth" appears twice in the title of D and once in a row header, this leads to two located occurrences, namely youth T, D and youth HR, D .

Then, for loc ∈ {T, HR, HC, C} we introduce a coefficient α loc allowing us to calibrate the weight (importance) of keyword occurrences in location loc. To quantify D's relevance for W due to its loc occurrences, we define a location score component f loc (D,W ). In particular, we have experimented with two f loc functions:

• f sum loc (D,W ) = α w ≺W scor e (w l oc, D ) loc • f count loc (D,W ) = α count {w ≺W } loc
where for score (w loc, D ) we use the value score (w ), the score of D with respect to w (Section 3.3). Thus, each f loc "boosts" the relevance scores of all loc occurrences by a certain exponential formula, whose relative importance is controlled by α loc . Further, the relevance of a dataset increases if different query keywords appear in different header locations, that is, some in HR (header rows) and some in HC (header columns). In such cases, the data cells at the intersection of the respective rows and columns may provide very precise answers to the query, as illustrated in Figure 1: here, "France" is present in HC while "youth" and "17" appear in HR.

To reflect this, we introduce another function f H (D,W ) computed on the scores of all unique located occurrences from row or column headers; we also experimented with the two variants, f sum H and f count H introduced above. Putting it all together, we compute the content-and location-aware relevance score of a dataset for W as:

д 2 (D,W ) = д 1 (D,W ) + Σ loc ∈ {T,HR,HC,C} f loc (D,W ) + f H (D,W )
Finally, we also experimented with another function д * (D,W ) defined as:

д * 2 (D,W ) =      д 2 (D,W ), if f T (D,W ) > 0 0, otherwise д *
2 discards datasets having no relevant keyword in the title. This is due to the observation that statistic dataset titles are typically carefully chosen to describe the dataset; if no query keyword can be connected to it, the dataset is very likely not relevant.

Data cell search

We now consider the problem of identifying the data cell(s) (or the data rows/columns) that can give the most precise answer to the user query.

Such an answer may consist of exactly one data cell. For example, for the query "unemployment rate in Paris", a very good answer would be a data cell D r,c whose closest row header cell contains "unemployment rate" and whose closest column header cell contains "Paris". Alternatively, query keywords may occur not in the closest column header cell of D r,c but in another header cell that is its ancestor in D. For instance, in Figure 1, let D r,c be the data cell at the intersection of the Aug-17 column with the France row: the word "youth" occurs in an ancestor of the Aug-17 header cell, and "youth" clearly characterizes D r,c 's content. We say the closest (row and column) header cells of D r,c and all their ancestor header cells characterize D r,c .

Another valid answer to the "unemployment rate in Paris" query would be a whole data row (or a whole column) whose header contains "unemployment" and "Paris". We consider this to be less relevant than a single data cell answer, as it is less precise.

We term data cell answer an answer consisting of either a data cell, or a row or column; below, we describe how we identify such answers.

We identify data cells answers from a given dataset D as follows. Recall that all located occurrences in D, and in particular those of the form w HR and w HC for w ≺ W , have been pre-computed; each such occurrence corresponds either to a header row r or to a header column c. For each data cell D r,c , we define #(r , c) as the number of unique words w ≺ W occurring in the header cells characterizing D r,c . Data cells in D may be characterized by:

(1) Some header cells containing HR occurrences (for some w ≺ W ), and some others containing HC occurrences; (2) Only header cells with HR occurrences (or only header cells with HC ones). Observe that if D holds both cell answers (case 1) and row-or column answers (case 2), by definition, they occur in different rows and columns. Our returned data cell answers from D are:

• If there are cells in case 1, then each of them is a data cell answer from D, and we return cell(s) with highest #(r, c) values. • Only if there are no such cells but there are some relevant rows or columns (case 2), we return the one(s) with highest #(r , c) values. This is motivated by the intuition that if D has a specific, one-cell answer, it is unlikely that D also holds a less specific, yet relevant one. Concretely, we compute the #(r , c) values based on the (word, score, location, dataset) tuples we extract (Section 3.3). We rely on SPARQL 1.1 [START_REF] W3c | SPARQL Protocol and RDF Query Language[END_REF] queries on the RDF representation of our datasets (Section 2) to identify the cell or row/column answer(s) from each D. SPARQL 1.1 features property paths, akin to regular expressions; we use them to identify all the header cells characterizing a given D r,c .

Note that this method yields only one element (cell, row or column) from each dataset D, or several elements if they have the exact same score. An alternative would have been to allow returning several elements from each dataset; then, one needs to decide how to collate (inter-rank) scores of different elements identified in different datasets. We consider that this alternative would increase the complexity of our approach, for unclear benefits: the user experience is often better when results from the same dataset are aggregated together, rather than considered as independent. Suggesting several data cells per dataset is then more a question of result visualization than one pertaining to the search method.

EVALUATION

This section describes our experimental evaluation. Section 4.1 describes the dataset and query workload we used, which was split into a development set (on which we tuned our score functions) and a test set (on which we measured the performance of our algorithms). It also specifies how we built a "gold-standard" set of answers against which our algorithms were evaluated. Section 4.2 details the choice of parameters for the score functions.

Datasets and Queries

We have developed our system in Python (61 classes and 4071 lines). Crawling the INSEE Web site, we extracted information out of 19,984 HTML tables and 91,161 spreadsheets, out of which we built a Linked Open Data corpus of 945 millions RDF triples.

We collected all the articles published online by the fact-checking team "Les Décodeurs",8 a fact-checking and data journalism team of Le Monde, France's leading national newspaper, between March 10th and August 2nd, 2014; there were 3,041 articles. From these, we selected 75 articles whose fact-checks were backed by INSEE data; these all contain links to https://www.insee.fr. By reading these articles and visiting their referenced INSEE dataset, we identified a set of 55 natural language queries which the journalists could have asked a system like ours. 9We experimented with a total of 288 variants of the д 2 function:

• д 1 was either д 1a , д 1b or д 1c ;

• д 2 relied either on f sum loc or on f count loc ; for each of these, we tried different value combinations for the coefficients α T , α HC , α HR and α C ; • we used either the д 2 formula, or its д * 2 variant. We built a gold-standard reference to this query set as follows. We ran each query q through our dataset search algorithm for each of the 288 д 2 functions, asking for k = 20 most relevant datasets. We built the union of all the answers thus obtained for q and assessed the relevance of each dataset as either 0 (not relevant), 1 ("partially relevant" which means user could find some related information to answer their query) or 2 ("highly relevant" which means user could find the exact answer for their query); a Web front-end was built to facilitate this process.

Experiments

We specify our evaluation metric (Section 4.2.1), then describe how we tuned the parameters of our score function, and the results of our experiments focused on the quality of the returned results (Section 4.2.2). Last but not least, we put them into the perspective of a comparison with the baselines which existed prior to our work: IN-SEE's own search system, and plain Google search (Section 4.2.3).

Figure 2: Screen shot of our search tool. In this example, the second result is a full column; clicking on "Tous les résultats" (all results) renders all the cells from that column.

Evaluation Metric.

We evaluated the quality of the answers of our runs and of the baseline systems by their mean average precision (MAP 10 ), widely used for evaluating ranked lists of results.

MAP is traditionally defined based on a binary relevance judgment (relevant or irrelevant in our case). We experimented with the two possibilities:

• MAP h is the mean average precision where only highly relevant datasets are considered as relevant • MAP p is the mean average precision where both partially and highly relevant datasets are considered relevant. We also experimented with some modified variants that take into account the sum of matching keywords: 1). • We computed the MAP scores obtained on the full development set for all 288 combinations of parameters, and plotted them from the best to the worst (Figure 3; due to the way we plot the data, two MAP h and MAP p values shown on the same vertical line may not correspond to the same score function).

The figure shows that the best-performing 15 combinations leads to scores higher than 0.80, indicating that any of these could be used with pretty good results.

These two results tend to show that we can consider our results as stable, despite the relatively small size of the query set.

Running time. Processing and indexing the words (close to those) appearing in the datasets took approximately three hours. We ran our experiments on a machine with 126GB RAM and 40 CPUs Intel(R) Xeon(R) E5-2640 v4 @ 2.40GHz. The average query evaluation time over the 55 queries we identified is 0.218 seconds.

Comparison against Baselines.

To put our results into perspective, we also computed the MAP scores of our test query set on the two baselines available prior to our work: INSEE's own dataset search system 11 , and Google search instructed to look only within the INSEE web site. Similarly to the evaluation process of our system, for each query we selected the first 20 elements returned by these systems and manually evaluated each dataset's relevance to the given query. Table 2 depicts the MAP results thus obtained, compared against those of our system. Google's MAP is very close to ours; while our work is obviously not placed as a rival of Google in general, we view this as validating the quality of our results with (much!) smaller computational efforts. Further, our work follows a white-box approach, whereas it is well known that the top results returned by Google are increasingly due to other factors beyond the original PageRank [START_REF] Brin | The Anatomy of a Large-Scale Hypertextual Web Search Engine[END_REF] information, and may vary in time and/or with result personalization, Google's own A/B testing etc.

We end this comparison with two remarks. (i) Our evaluation was made on INSEE data alone due to the institute's extensive database on which fact-checking articles were written, from which we derived our test queries. However, as stated before, our approach could be easily adapted to other statistic Web sites, as we only need the ability to crawl the tables from the Web site. As is the case for INSEE, this method may be more robust than using the category-driven navigation or the search feature built in the Web site publishing the statistic information. (ii) Our system, based on a fine-granularity modeling of the data from statistic tables, is the only one capable of returning cell-level answers (Section 3.5). We show such answers to the users together with the header cells characterizing them, so that users can immediately appreciate their accuracy (as in Figure 2).

RELATED WORK AND PERSPECTIVES

In this work, we focused on how to improving the usability statistic tables (HTML tables or spreadsheets) as reference data sources against which claims can be fact-checked. Other works focused on building textual reference data source from general claims [START_REF] Bar-Haim | Stance Classification of Context-Dependent Claims[END_REF][START_REF] Levy | Context Dependent Claim Detection[END_REF], congressional debates [START_REF] Thomas | Get out the vote: Determining support or opposition from Congressional floor-debate transcripts[END_REF] or tweets [START_REF] Rajadesingan | Identifying Users with Opposing Opinions in Twitter Debates[END_REF].

Some works focused on exploiting the data in HTML and spreadsheet tables found on the Web. Tschirschnitz et al. [START_REF] Tschirschnitz | Detecting Inclusion Dependencies on Very Many Tables[END_REF] focused on detecting the semantic relations that hold between millions of Web tables, for instance detecting so-called inclusion dependencies (when the values of a column in one table are included in the values of a column in another table). Closest to our work, M. Kohlhase et 11 Available at https://insee.fr al. [START_REF] Kohlhase | XLSearch: A Search Engine for Spreadsheets[END_REF] built a search engine for finding and accessing spreadsheets by their formulae. This is less of an issue for the tables we focus on, as they contain plain numbers and not formulas.

Google's Fusion Tables work [START_REF] Gonzalez | Google Fusion Tables: Data Management, Integration, and Collaboration in the Cloud[END_REF] focuses on storing, querying and visualizing tabular data, however, it does not tackle keyword search with a tabular semantics as we do. Google has also issued Google Tables as a working product 12 . In March 2018, we tried to use it for some sample queries we addressed in this paper, but the results we obtained were of lower quality (some were irrelevant). We believe this may be due to Google's focus on data available on the Web, whereas we focus on very high-quality data curated by INSEE experts, but which needed our work to be easily searchable.

Currently, our software is not capable of aggregating information, e.g., if one asks for unemployed people from all departements within a region, we are not capable of summing these numbers up into the number corresponding to the region. This could be addressed in future work which could focus on applying OLAP-style operations of drill-down or roll-up to further process the information we extract from the INSEE datasets.

Figure 1 :

 1 Figure 1: Sample dataset on French youth unemployment. RDF data extracted from the INSEE statistics INSEE publishes a set D of datasets D 1 , D 2 , . . . etc. where each dataset D i consists of

  (w, score (w ), location(w, D), D) where location(w, D) ∈ {T, HR, HC, C} indicates where w appears in D: T denotes the title, HR denotes a row header cell, HC denotes a column header cell, and C denotes an occurrence in a comment.

4. 2 . 2

 22 Parameter Estimation and Results. We experimented with the following flavors of the д 1 function :• д 1b (D,W ) = 10 w ≺W scor e (w ) • д 1d (D,W ) = 10 count {w ≺W } • д 1f (D,W ) = w ≺W10 scor e (w )

•

  д 1c (D,W ) = w ≺W score (w ) + д 1b (D,W ) • д 1e (D,W ) = w ≺W score (w ) + д 1d (D,W ) • д 1д (D,W ) = w ≺W score (w ) + д 1f (D,W )A randomly selected development set of 29 queries has been used to select the best values for the 7 parameters of our system : α T , α C , α HR , α HC , α H , as well as the different versions of д 1 and д 2 . For this purpose, we ran a grid search with different values of these parameters, selected among {3, 5, 7, 8, 10}, on the development query set, and applied the combination obtaining the best MAP results on the test set (composed of the remaining 26 queries).We found that a same score function has lead to the best MAP h and the best MAP p on the development query set. In terms of the notations introduced in Section 3.4, this best-performing score function is obtained by:• Using д 1,c ; • Using f sumand the coefficient values α T = 10, α C = 3, α HR = 5, α HC = 5 and α H = 7; • Using the д * 2 variant, which discards datasets lacking matches in the title. On the test set, this function has lead to MAP p = 0.76 and MAP h = 0.70 .10 https://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision

Figure 3 :

 3 Figure 3: MAP results on the development set for 288 variants of the score function.

  . . etc. where each dataset D i consists of a table (HTML table or a spreadsheet file), a title D i .t and optionally an outline D i .o. The title is a short nominal phrase stating what D i contains, e.g., "Seasonally adjusted youth (under 25s) unemployment" in Figure

Table 1 :

 1 Results on the first and second development set.Given that our test query set was relatively small, we performed two more experiments aiming at testing the robustness of the parameter selection on the development set:• We used a randomly selected subset of 17 queries among the 29 development queries, and used it as a new development set. The best score function for this new development set was the same; further, the MAP results on the two development sets are very similar (see Table

		Dev. set 29 queries Dev. set 17 queries
	MAP p	0.82	0.83
	MAP h	0.78	0.80

Table 2 :

 2 Comparing our system against baselines.

		Our system INSEE search Google search
	MAP p	0.76	0.57	0.76
	MAP h	0.70	0.46	0.69

https://gitlab.inria.fr/cedar/insee-crawler, https://gitlab.inria.fr/cedar/excel-extractor

https://jena.apache.org/documentation/tdb/index.html

http://ec.europa.eu/eurostat/statistics-explained/images/8/82/Extra_tables_Statis-tics_explained-30-11-2017.xlsx

This assumption is backed by an overwhelming majority of cases given the nature of statistic data. We did encounter some counterexamples, e.g. http://ec.europa.eu/eurostat/cache/metadata/Annexes/mips_sa_esms_an1.xls. However, these are very few and thus we do not take them into account in our approach.

https://github.com/boudinfl/kea

http://www.geonames.org/

http://www.lemonde.fr/les-decodeurs/

This was not actually the case; our system was developed after these articles were written.

https://research.google.com/tables