Marine Salès

Do Corporate Credit Conditions Alter Labor Market Dynamics? A SVAR Analysis in a Transatlantic Perspective

Keywords: SVAR, credit shocks, productivity shocks, labor market dynamics

The purpose of this paper is to establish the eects of credit and technological shocks on unemployment and vacancies in United-States and Germany. A structural VAR methodology is used in a macro-econometric setting to achieve this. Shocks are identied based on the assumption that working capital is paid in advance of production as it is assumed in various theoretical papers. The results show that technological shocks have a positive impact on employment and vacancies in both countries, with a larger impact in United-States than in Germany. Credit shocks appear to aect dierently both countries. In United-States, a positive credit shock leads to an increase in employment and vacancies, while in Germany the opposite eect is obtained for employment and vacancies, even if the impact on vacancies is insignicant. A common view widespread today is to consider that more credit in one economy will be the source of better labor markets conditions as it eases external nancial constraints. My empirical results suggest that this view can be challenged and discussed as an increase in the level of credit in an economy does not necessarily lead to a better conditions on labor markets. Finally, a credit shock has a quite strong positive impact on output in United-States, whereas this impact is also positive for Germany but weaker and not signicant, consistent with the idea that credit conditions is not sucient to improve economic situations in this country.

Introduction

The Great Recession is associated with a sharp disruption in credit markets together with a marked deterioration in labor markets in a lot of developed countries. However, dierences are observed between countries, especially between two countries that are Germany and the United-States. The relationship between credit market conditions and labor markets is commonly seen as being positive, meaning that better credit market conditions lead to better labor markets conditions. Theoretical papers [START_REF] Monacelli | Financial markets and unemployment[END_REF], [START_REF] Perri | International recessions[END_REF], [START_REF] Zanetti | Financial shocks and labor market uctuations[END_REF], [START_REF] Iliopulos | Welfare cost of uctuations: when labor market search interacts with nancial frictions[END_REF], [START_REF] Petrosky-Nadeau | Credit, vacancies and unemployment uctuations[END_REF], [START_REF] Garin | Borrowing constraints, collateral uctuations, and the labor market[END_REF]) are showing this result for the United-States. However, from a strict empirical point of view, it has not been yet found, especially for other countries than the United-States.

This paper aims to investigate the patterns of unemployment and vacancies dynamics in Germany and in United-States after a technological and a credit shock. So the paper is presenting evidence on labor market dynamics in each country and compares them by using a structural VAR model. The labor market dynamics vary substantially, in terms of magnitude, persistence, signs and signicance, with the identied shocks depending on the country considered.

I nd that technological shocks turn out to be relatively important for variations in unemployment and vacancies in both countries, leading to an increase of vacancies and a decrease of unemployment. The eect is more persistent and stronger for the United-States than for Germany. Finally, technological shocks impact positively the credit in both countries, even if it is not signicant for Germany and quite weak and short lasting for the United-States.

For credit shocks, I nd that they aect dierently both countries. In United-States, a positive credit shock leads to an increase in employment and vacancies, while in Germany the opposite eect is obtained for employment and vacancies, even if the impact on vacancies is insignicant. A common view widespread today is to consider that more credit in one economy will be the source of better labor markets conditions. My empirical result suggests that this view can be challenged and discussed as an increase in the level of credit in an economy is not necessarily the source of a better situation in labor markets. Finally, a credit shock has a quite strong positive impact on output in United-States, whereas this impact is also positive for Germany but weaker and not signicant, consistent with the idea that credit conditions is not sucient to improve economic situations in this country.

First, to motivate the empirical analysis, I consider the following graphs. They depict the correlation between unemployment rate, vacancies and non-nancial corporations credit between 1952:Q1 and 2014:Q4 for the United-States and 1971:Q1 and 2014:Q4 for Germany1 . For the United-States, a negative correlation (-0.38) between unemployment and corporations credit is found, as well as a positive correlation (0.33) between vacancies and corporations credit. The higher is the growth of credit for corporations in United-States, the lower is the unemployment rate and the higher are job vacancies. The relationship between these variables for Germany appears to be null for unemployment and corporations credit, and slightly positive for vacancies and corporations credit. It brings to ask whether corporate credit conditions alter always labor market dynamics in every countries, especially in Germany and in the United-States. 1952 1959 1966 1973 1980 1987 1994 2001 2008 2015 Quarters Unemployment Non-financial corporations credit growth Vacancies 1952 1959 1966 1973 1980 1987 1994 2001 2008 2015 Quarters Vacancies Non-financial corporations credit growth 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Quarters

Vacancies Non-financial corporations credit

Germany is a country worth to consider due to availability of data and due to dierences in labor market structures compared to the United-States. [START_REF] Bachmann | What drives labor market dynamics in the us and germany? Barnichon, R[END_REF] are also comparing Germany and United-States to determine the eects of technological shocks on labor market dynamics. They nd signicant dierences between these two countries. [START_REF] Sala | Structural and cyclical forces in the labor market during the great recession: Cross-country evidence[END_REF] investigate also dierences across countries about labor market dynamics after the Great Recession. They show that Germany follows dierent dynamics compared to other countries and especially the United-States. From an empirical point of view, they show that unemployment in Germany tends to decline since 2005, as we observe on the previous graph, with just a relatively small increase during the crisis, at least compared to the United-States. Unemployment is even lower than before the nancial crisis. The authors claim that these countries may have been hit by dierent types of shocks, especially by the risk-premium shock. I investigate in this paper the role potentially played by a so-called credit shock dened latter.

To this purpose, I use a structural vector-autoregressive model where restrictions are imposed on the response of variables on each other based on an underlying VAR model with the objective to obtain a causal meaning with impulse response functions and variance decomposition. To identify properly each shock, I propose shortterm restrictions that are based on assumptions made in various recent theoretical papers [START_REF] Kiyotaki | Credit Cycles[END_REF], [START_REF] Monacelli | Financial markets and unemployment[END_REF], [START_REF] Perri | International recessions[END_REF], [START_REF] Jermann | Macroeconomic eects of nancial shocks[END_REF], [START_REF] Zanetti | Financial shocks and labor market uctuations[END_REF], [START_REF] Iliopulos | Welfare cost of uctuations: when labor market search interacts with nancial frictions[END_REF], Petrosky-Nadeau (2014), [START_REF] Garin | Borrowing constraints, collateral uctuations, and the labor market[END_REF]), claiming that external nancing is necessary prior to production to pay in advance working capital. Hence, the level of corporations credit in one economy is seen in those models as essential to enable rms' hiring and to determine the level of employment in the economy. I propose to confront this assumption to empirical data by considering two countries that have dierent institutional environment, both on labor markets as on credit markets.

2 How corporate credit conditions may alter labor market dynamics?

Corporations are using credit to nance their production costs in advance. They have to pay for vacancies posting costs and wages in particular for labor markets. A high credit level in one economy can be the source of higher employment and lower job vacancies as rms can nance externally easily costs linked to recruitment and wages. However, a high credit level in one economy can reect as the same time a higher external nancing dependency that can be seen by entrepreneurs as a risk for their nancial structure. As a consequence they can decide to reduce their employment and to post lower vacancies so as to restore their nancial situation. Furthermore, in a Keynesian point of view, a high credit level does not necessarily imply more hiring if corporations are not anticipating higher demand in the future.

Two opposite view in face of credit conditions are thus considered. The one that sees the credit as a way to relax the nancial constraint on rm that has to be used to develop the economic activity in ne. This view appears to be widespread today and theoretical models show this result for the United-States, inviting to consider the credit level as a tool or as an indicator of economic well-being. Another view is to see the credit as a way to relax the nancial constraint, as well as a signal to be cautious in the management of the labor force as the external nancing dependence increase.

VAR specication

The labor market dynamics of a country is assumed to be given by the dynamic system whose structural equations are:

A 0 X t = α + Σ p i=1 A i X t-i + t V ar( t ) = Σ (1)
where A 0 is an invertible (n × n) matrix describing instantaneous relations among variables; X t is a (n × 1)

vector of stationary endogenous variables; α is a vector of constants; A i is a (n × n) matrix of coecients and t is the vector of exogenous uncorrelated or orthogonal structural shocks to each variable. Σ is the matrix of variance-covariance of structural disturbances.

The vector of endogenous variables is dened as:

X t =          ∆c t ∆y t u t v t         
and accordingly, the vector of the orthogonalized disturbances:

t =          c,t y,t u,t v,t         
In my benchmark specication, I include as variables for the VAR of each country the rst-dierence of the log of non-nancial corporations credit (∆c t ), the rst-dierence of the log of the real output (∆y t ), the logs of unemployment rate (u t ) and vacancies rate (v t )6 . The use of rst dierences is determined by unit root tests whose results are found in appendix C. The augmented Dickey-Fuller (ADF) test indicates indeed a non-stationary behavior of non-nancial corporations credit and real output for both countries.

In comparison with an unrestricted VAR, an additional matrix A 0 of parameters appears. It multiplies the vector of contemporaneous endogenous variables X t and enables contemporaneous relationships between variables. From the structural equations (1), I consider the following reduced-form VAR model of order p:

X t = δ + Σ p i=1 B i X t-i + e t V ar(e t ) = A -1 0 Σ(A -1 0 ) (2)
where δ = A -1 0 α is a vector of constants, B i = A -1 0 A i and e t = A -1 0 t . e t is a (n × 1) vector of error terms.

Although estimating the equations of a standard VAR does not imply strong identication assumptions, computing impulse response functions or variance decomposition require identifying restrictions. A typical restriction takes the form of an assumption about the dynamic relationship between variables. So I have to transform the residuals from the reduced form VAR into structural residuals such that they can be interpreted.

I dene now the short-term restrictions based on theoretical assumptions to be able to interpret the results.

Identication of shocks

The conditional distribution of X t is completely characterized with the reduced-form VAR. The structural form of the model has thus n 2 additional parameters compared to the reduced form coming from the matrix A 0 .

An identication problem appears. Indeed, given values of the reduced form parameters δ, B i , V ar(e t ), it is not possible to uniquely solve the system for the structural parameters α, A 0 , A i , Σ. To go from the reduced form to the structural one, n 2 identifying restrictions have to be imposed.

More precisely, innovations e t from the reduced-form VAR are typically correlated. It represents the unpredictable shock in each endogenous variable of X t . So they could be correlated with one another because of contemporaneous causal relationships for example or because exogenous structural shocks are correlated with one another. It is as a consequence not possible to interpret them as structural shocks. Identifying restrictions are thus necessary.

The matrix A 0 relates the structural shocks to the reduced-form innovations:

A 0 e t = t (3)
So A 0 represents the immediate eects of shocks on X t .

The aim of the paper is to provide evidence on unemployment, vacancies dynamics in response to technological and credit shocks. The identication of the structural shocks is done through the imposition of zero short-term restrictions. Short-run restrictions contain assumptions about contemporaneous relations between shocks and variables and are thus imposed on matrix A 07 .

It is now common to normalize the main diagonal of A 0 to 1 and to consider Σ as being a diagonal matrix.

It ensures that each variables of X t is allocated to its own structural equation and allows to give an economic interpretation to each shock. With this 10 restrictions of normalization, I have to nd n(n-1) 2 other restrictions to identify technological and credit shocks.

I dene in the benchmark model the following matrix A 0 :

A 0 =          1 0 0 0 a yc 1 a yu a yv a uc 0 1 a uv a vc 0 0 1         
By imposing that some of the elements of A 0 are zero, I am assuming that some of the orthogonalized disturbances have no immediate impact on some of the variables. To identify properly a four dimensional system, I need at least six restrictions. So I need to impose that at least four elements are zero. To build the short-term restrictions, I use the following assumption: credit is necessary for rms to pay in advance of production the production costs, especially the hiring costs as well as wages. However, it is where credit frictions occurs and nancial constraints may appear as a consequence. So a credit shock may have a direct and immediate impact on the level of employment, vacancies posting and thus on real output. It is a fundamental assumption made here, stipulating that an exogenous shock on credit has a direct impact on labor market variables as the credit is used immediately to nance working capital. The identication scheme assumes that credit is predetermined and does not react to other shocks in the model within the same quarter. It depends only on its own history and on lagged values of other variables.

Thus, I propose as short-term restrictions consistent with this assumption, leading to the previous matrix:

1. Credit conditions evolve exogenously, technological and other shocks cannot aect contemporaneously the credit. Therefore, a cy = a cu = a cv = 0.

2. Innovations to productivity aect credit, unemployment and vacancies with one period lag. So a uy = a vy = 0.

3. The residual shocks to employment and vacancies are identied using a non-structural triangular restriction that requires that a vu = 0.

These restrictions enable to interpret y,t as innovations to technology, c,t as innovations to the availability of credit, u,t and v,t as innovations to unemployment and vacancies8 .

No restrictions are imposed on the eect of credit shocks on real output, vacancies and unemployment to not exclude short-term eects from credit shocks on the real economy as it may be when producers adjust their working capital depending on their access to external nancing.

Thus, unemployment is allowed to respond contemporaneously to innovations in credit and vacancies. There is no contemporaneous impact of output, unemployment and vacancies on credit. Output responds contemporaneously to innovations to credit, unemployment and vacancies. Finally, vacancies react contemporaneously to innovations in credit only.

I do not impose more restrictions that required by the just identication necessity9 .

Results

The results are based on an optimal lag order of p = 2 for both countries. Appendix C delivers the results of the selection criteria (nal prediction error (FPE), Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (SBIC), and the Hannan and Quinn information criterion(HQ)) that lead to choose this lag length. However, for the United-States tests are also selecting a higher lag order of 4. As a consequence, in robustness analysis, I discuss the results for 4 lags.

Impulse Responses

Impulse responses illustrate the dynamic response of a variable to a structural shock. The impulse are normalized to a one standard deviation in the underlying variable. Responses of the labor market variables are presented in percentage points.

Technology shock.

- In United-States, a positive technological shock (increase in the real output) lowers unemployment for 3 years.

The eect is largest after one year where the one standard error technological shock reduces unemployment by For the response of non-nancial corporations credit, technological shock triggers a signicant slight increase of one quarter. However, as the one standard error band includes zero and becomes wider with the horizon increase, this result may to not be signicant in the long run.

For Germany, the sign of results are similar even if one standard error bands are larger. After a technological shock, unemployment decreases for almost 3 years with a peak at 2 years (-0.025 pct). For the two rst quarters, the response of unemployment is borderline signicant. [START_REF] Nordmeier | Conditional patterns of unemployment dynamics in germany[END_REF] nd also that the unemployment rate goes down after a positive technological shock. Vacancies increase for two years after the shock with a peak one year and half at nearly 0.04 pct. Both eects on unemployment and vacancies die out hereafter. Finally, as for the United-States, the impact on non-nancial corporations credit is ambiguous as the one standard error band includes zero.

The mechanisms are the following consistent with standard Real Business Cycle models integrating search and matching frictions. A positive technological shock increases the expected prots from a match such that rms decide to post more vacancies and unemployment decreases as a consequence of the increase of labor market tightness from the rm point of view.

Credit shock. Figures 5 and6 present the dynamic adjustment process after a positive credit shock, implying an increase of non-nancial corporation credits.

Vacancies and employment increase after a positive credit shock in the United-States. A shock that increase the availability of credit positively aects the entire economy in that corporations have a higher access to credit to nance job vacancies and employment, and this leads to an increase in production and hence the gross domestic product increase with an immediate peak at 0.3 pct. The unemployment decreases signicantly until 5 quarters in response to a rise in the non-nancial corporations credit and it then adjusts gradually to the steady state.

For Germany, a positive credit shock leads to an increase in unemployment and a decrease in vacancies. The response of unemployment is signicant for almost ve quarters and is quite persistent, while the response of vacancies is insignicant, suggesting to not overstate the impact of credit shock on vacancies in Germany, but I can notice that the response is shown to be negative. Finally, the impact of a positive credit shock on the output in Germany is surprisingly insignicant, even if the impact is positive but weaker compared to the result founded for the United-States. Finally, I plot the cumulative eect of a permanent shock to real output and non-nancial corporations credit 10 . For United-States, a permanent positive shock of one standard deviation to real output causes unemployment to be about 0.4 percentage points lower. This magnication comes from two eects. First, shocks to technology tend to persist for periods after the shock, so unemployment decreases more as a result. Second, a positive shock to technology increases employment, which feeds back positively on real output. Then, a permanent positive shock to real output causes vacancies to be about 0.15 percentage points higher. For the credit, the impact is about 0.4 percentages point but not signicant anymore after 4 quarters.

For Germany, a permanent positive shock of one standard deviation to real output triggers unemployment to be about 0.3 percentage point lower and vacancies 0.3 higher. Results for credit are not signicant. A permanent shock to credit has a signicant negative impact on unemployment of 0.3 percentage points in United-States and a positive impact on vacancies of almost 0.2 percentage points, whereas for Germany, it leads to a signicant positive impact on unemployment of 0.3 percentage points and no signicant impact on vacancies, even if the impact is negative.

10 Figures are found in appendix D.

High levels of credit in an economy are surely a tool for American rms to recruit more and it leads to an increase in real output thanks to new production that is possible. However, for Germany according to our results, the eect of credit on labor market and on output is not clear. I nd no signicant impact of credit on output, nor on vacancies. I nd however a signicant positive impact on unemployment.

On the other side, a positive technological shock is found to have a statistically signicant negative impact on unemployment in both countries, higher in United-States, and a signicant positive impact on vacancies. For both countries, no signicant impact (except a positive one for the United-States) of a technological shock on credit is found.

Variance Decomposition of Unemployment and Vacancies

The variance decomposition of the forecast errors establishes the relevance of shocks for variations in unemployment and vacancies. It shows the extent to which shocks contributes to unexpected movements in each variables.

Tables 1 and2 give the proportions of variations in unemployment and vacancies due to the two dierent structural shocks. The two shocks account for respectively approximately 20% and 18% of the forecast error variance in unemployment and vacancies in Germany, and for respectively 61% and 36% of the forecast error variance in unemployment and vacancies in United-States. In both countries, technological shock plays a key role.

A technological shock has only a small eect on unemployment initially in Germany. However, after two years, technological shock explain 11.4 pct. of the unemployment uctuations in Germany, increasing to 13 pct.

after 3 years.

In the short term, technological shock is the most important source of labor market uctuations in United-States with 33-45 pct. of the variance in unemployment explained the rst year, and 23-35 pct. of the variance in vacancies. The relative contribution of technological shock continues to increase until 2 years and then begins to decline slowly. Especially for unemployment dynamic in United-States credit disturbances become more important with the forecast horizon, reaching 15.3 pct. However, for vacancies dynamics, the impact is surprisingly almost negligible (less than 1 pct.).

Hence, between both countries, the positive eects of a credit shock on unemployment and vacancies appear larger in the United-States than in Germany. This large contribution of credit shocks to the United-States economy stands in coherence with the way rms are nancing their working capital in both countries. The structure of economies play probably an important role for the macroeconomic adjustments to credit shocks. A country where rms have a low dependence to external nancing may suer less from a credit shock. Germany seems to use the lever of the credit as a way to reduce their working capital to face future periods of diculties. Results are available upon request. For Germany, signs are not changing. However, I loose completely the 11 The null hypothesis can be rejected at the 5% level. For Germany, I use as labor productivity the real labor productivity per hours worked from Eurostat. I limit the analysis to 2014:1 due to data availability. I obtain the same signs for results, even if they are again less signicant.

Finally, I test the results by using the non-nancial corporations credit as a percentage of the GDP from the BIS. Results obtained are also coherent with the previous one and available upon request.

Lag length. I re-estimate the benchmark model with a higher lag of p = 4 for the United-States, as suggested by three selection criteria.

The key results remain unchanged. The only notable change concerns the impact of a credit shock on unemployment that is more long-lasting. Indeed, the eect is signicant until 14 quarters (compared to 9 quarters in the benchmark specication).

Sub-sample analysis. I investigate here the sub-sample stability of the preceding results. I restrict the data until the beginning of the crisis at the end of 2007 14 . The aim is to avoid the introduction of specic eects due to the Great Recession and to see how much this specic period played a key role. Responses for United-States do not change. However, for Germany, no result are signicant (even for the technological shock), and responses of unemployment and vacancies to a credit shock change notably. I observe a slight decrease of unemployment (less than -0.01 pct. at the peak at 6 quarters) and an increase of vacancies (reaching a peak at 0.02 pct. at 4 quarters). As a consequence, it appears that the crisis may have modify the way rms are considering the credit in Germany.

12 I test for two sources of labor productivity, comprising the rst-dierence of the logarithm of the real output per hour of all persons from the U.S Bureau of Labor Statistics. As results were similar, I choose to include here only one 13 See appendix A for data denitions and sources. 14 According to NBER recessions date. The Great Recession begins according to OECD recessions date in February 2008 in Germany. I decide to restrict the data for Germany also at the end of 2007.

Identifying assumptions. First, I test the following restrictions:

a cy = a cu = a cv = a uy = a vy = a uv = 0 (4)
The results are absolutely identical for both countries.

Then, I tried other identifying strategies based on a dierent assumption. Credit is not pre-determined anymore and all other shocks can impact this variable. On the other side, credit shocks and other shocks cannot aect the level of the output within the same period. Finally, I test for various schemes for the last two other variables.

However, either the regressions do not converge, or identication problems appear.

7 The eect of a credit shock on other macro variables I investigate here the inuence of a credit shock on other macroeconomic variables that are not initially in the benchmark model. I order this variable at last, meaning that they have no contemporaneous impact on other variables.

Consumption. For the United-States, consumption is dened as the personal consumption expenditures from the Bureau of Economic Analysis. For Germany, it corresponds to the private nal consumption expenditure from OECD. Consumption is deated and divided by labor force index to obtain real per capita consumption 15 .

Four identifying restrictions have to be set to identify the ve dimensional SVAR model. I make the following restrictions:

Figures 7 and8 show the impulse response of both shock on consumption for both countries for SVAR models of lags 2 according to selection criteria. States with a peak at more than 0.2 pct. For Germany, the impact is positive but insignicant, that is coherent with the previous ndings on real output of a credit shock.

Conclusion

This paper investigates the labor market dynamics in Germany and United-States after a technological and a credit shock. Higher levels of non-nancial corporate credit have various impact on labor markets depending on countries. In United-States, a positive credit shock has a positive impact on the labor market, through a decrease of unemployment and an increase in vacancies. In Germany, a positive credit shock has a negative impact on the labor market, through an increase of unemployment and a decrease of vacancies, even if the impact on vacancies is insignicant. A common view widespread today is to consider that more credit in one economy will be the source of better labor markets conditions. My empirical result suggests that this view can be challenged and discussed as an increase in the level of credit in an economy is not necessarily the source of a better situation in labor markets.

B Data description

Figure 1 :

 1 Figure 1: Unemployment, job vacancies and non-nancial corporations credit growth between 1952-Q1 and 2014-Q4 for United-States

Figure 2 :

 2 Figure 2: Unemployment, job vacancies and non-nancial corporations credit growth between 1991-Q1 and 2014-Q4 for Germany

  As a preliminary check, I verify that series are stationary. Results of the augmented Dickey-Fuller tests are in appendix C for variables expressed either in level or in rst-dierence. I include a number of lagged dierences according to the Schwarz and Hannan-Quinn information criteria to eliminate serial correlation in the error terms of the Dickey-Fuller regression. When data show a clear trend, I use the trend option. Phillips-Perron tests lead to similar conclusions. All series of variables used in the VAR are shown to be stationary as the tests do not detect the presence of unit roots 5 . 4 Empirical model I investigate the importance of credit and technological shocks empirically for two countries that have dierent economic institutions, namely the United-States and Germany. I use a four dimensional SVAR model (integrating the credit to non-nancial corporations, real output, unemployment and vacancies) to achieve this by using a framework with a minimum of theoretical assumptions. Thus, it enables to address dierent ideas concerning the sources and patterns of labor market dynamics.First, I specify the VAR model and then, I identify structural shocks based on theoretical assumptions. Indeed, unrestricted VAR is on reduced form and innovations generated are thus uninterpretable. So to go from the reduced form to a structural model, identifying restrictions has to be imposed on the variables of the model. So I develop a structural VAR where identifying restrictions are based on a structural assumption usually made in macroeconomic theory, meaning that working capital are paid in advance of production. As a consequence, this assumption can trigger new empirical insight on the relationship between labor and credit markets.

Figure 3 :

 3 Figure 3: United States -Orthogonalized IRF to a positive technological shock

Figure 6 :

 6 Figure 6: Germany -Orthogonalized IRF to a positive credit shock

  This section tests the previous results along various dimensions. First, I address some data issues by considering unemployment and vacancies in rst-dierence, labor productivity instead of real output and non-nancial corporations credit as a percentage of the GDP. Then, I proceed by modifying the lag length. Finally, I inspect the identifying assumption.Data denitions. Because the ADF test cannot reject optimally the null hypothesis of non stationnarity for the unemployment and vacancies in Germany 11 , I check the results by including these variables in rst-dierences.

  results signicance. By computing the cumulative impulse response functions, I nd some signicant results for the impact of a technological shock on vacancies and unemployment as found previously. For the United-States, results are in line of what previously obtained being signicant.Then, I use another denition than the real output by considering the labor productivity. For the United-States 12 , it corresponds to the rst-dierence of the logarithm of the real GDP divided by the product of the average hours per worker in the non-farm business sector and the employment population 13 . I obtain the same results. No signicant change can be observed.

Figure 8 :

 8 Figure 8: Orthogonalized IRF to a positive credit shock

  It could lead to not increasing employment or job vacancies, or even to reduce them to reimburse previous loans, or to invest more in capital for example, if the labor market institutions are not enough exible to ensure in the future an adjustment. As observed since the Great Recession, a high level of credit in an economy do not lead necessary to an increase of employment. It could be the sign of a higher

	nancial fragility.
	3 Data description
	Data used are quarterly data for the period 1952 -2014 for the United-States and for the period 1991 -2014
	for Germany 2 . The time period dierence is due to data availability for Germany and to avoid the structural
	break due to reunication.
	For the United-States, the unemployment rate is the civilian unemployment rate from the Bureau of Labor
	Statistics (BLS). It corresponds to the number of unemployed as a percentage of the labor force. Vacancies
	are the index of help-wanted advertising in newspapers and online from the paper of Barnichon (2010), divided
	by a labor force index. Unemployment rate and vacancies are both expressed in logarithm. The real output
	is the real gross domestic product, billions of chained 2009 dollars from the US. Bureau of Economic Analysis
	(BEA). It is divided by the labor force index and expressed in rst-dierence of logarithm. The non-nancial
	corporations credit data are obtained from the BIS total credit statistics 3 . Data are divided by price level and
	labor force index and expressed in rst-dierence of logarithm. Finally, the price level is the GDP deator from
	the BEA.

For Germany, unemployment rate is the unemployment rate for persons aged 15 and over from the Organization for Economic Cooperation and Development (OECD). It corresponds to the number of unemployed as a percentage of the labor force. Vacancies correspond to an index based on the total unlled job vacancies from OECD 4 , divided by a labor force index. Unemployment rate and vacancies are both expressed in logarithm.

The real output is the real gross domestic product, billions of chained 2010 euros from Eurostat. It is divided by labor force index and expressed in rst-dierence of logarithm. The non-nancial corporations credit data are obtained from the BIS total credit statistics. Data are divided by price level and labor force index, and expressed in rst-dierence of logarithm. Finally, the price level is the GDP deator from Eurostat.

Table 1 :

 1 Forecast error variance decomposition for Germany

		Unemployment		Vacancies
	Forecast horizon Techn. shock Credit shock Techn. shock Credit shock
	1	3.8e-08	3.744	2.7416	0.1769
	2	0.2422	4.8395	11.6231	0.1251
	3	2.9165	7.9704	13.303	0.3369
	4	5.5232	9.4975	14.2708	0.8195
	8	11.4518	10.4231	15.4623	2.5239
	12	13.0614	10.5995	15.803	3.643
	16	13.5164	11.0323	15.8102	4.1463
	20	13.5916	11.4518	15.7422	4.2686

Table 2 :

 2 Forecast error variance decomposition for United-States

		Unemployment		Vacancies
	Forecast horizon Techn. shock Credit shock Techn. shock Credit shock
	1	33.3495	9.0572	23.0065	0.36479
	2	39.8723	9.7548	29.8035	0.47706
	3	43.1568	10.708	33.7515	0.61736
	4	44.9307	11.4704	35.6363	0.70329
	8	46.9259	13.9232	36.4409	0.88127
	12	46.7163	15.1946	35.1079	0.89746
	16	46.0845	15.4617	34.7019	0.87743
	20	45.4726	15.3838	34.7127	0.8777
	6 Robustness analysis

Table 7 :

 7 SVAR Lag Order Selection by Selection Criteria for United-States Likelihood ratio test statistics, FPE = Final prediction error, AIC = Akaike information criterion, SBIC = Schwarz bayesian information criterion, HQ = Hannan-Quinn information criterion.

	Maximum Lag Length LR FPE AIC SBIC HQ
	2	2	2	2	2	2
	4	4	4	4	2	2
	6	6	4	4	2	2
	10	10 4	4	2	2
	Notes: LR =

Table 8 :

 8 SVAR Lag Order Selection by Selection Criteria for Germany Likelihood ratio test statistics, FPE = Final prediction error, AIC = Akaike information criterion, SBIC = Schwarz bayesian information criterion, HQ = Hannan-Quinn information criterion.

	Maximum Lag Length LR FPE AIC SBIC HQ
	2	2	2	2	2	2
	4	4	2	2	2	2
	6	5	2	2	2	2
	10	9	2	2	2	2
	Notes: LR =

Details on data are found in section 3 or in appendix A.

See appendix A for data denitions and sources.

From the BIS, credit is provided by domestic banks, all other sectors of the economy and non-residents. Non-nancial corporations (both private-owned and public-owned) are dened according to the System of National Accounts 2008. In terms of nancial instruments, credit covers loans and debt securities. The series have quarterly frequency and capture the outstanding amount of credit at the end of the reference quarter.

 4 From OECD, the job vacancies data provide estimates of the number of unlled job vacancies. Series give an indication of the labor demand.

However, as tests for unemployment and vacancies for Germany appear to be less signicant, I test the results in robustness check for the inclusion of unemployment and vacancies in rst-dierence.

See again appendix A for exact denitions and sources of variables.

Long-run restrictions are imposed on impulse responses.

An other way to obtain a just-identied system is to assume for residual shocks that auv = 0. Results based on this alternative identication scheme are similar as shown in robustness analysis.

[START_REF] Sims | Macroeconomics and reality[END_REF] considers in his famous critic that over-identications are entirely unrealistic.

Appendix A for data denitions and sources.

Appendices

A Data denitions and sources

C SVAR Model Specication


William Schueller
email: william.schueller@inria.fr

Pierre-Yves Oudeyer
email: pierre-yves.oudeyer@inria.fr

ACTIVE CONTROL OF COMPLEXITY GROWTH IN NAMING GAMES: HEARER'S CHOICE

How do linguistic conventions emerge among a population of individuals? A shared lexicon can self-organize at population level through local interactions between individuals, what has been shown in the Naming Games computational framework. However, the dynamics of the convergence towards this shared convention can differ a lot, depending on the interaction scenario. Infants, who acquire social conventions really fast, control actively the complexity of what they learn, following a developmental pathway. Adults also adapt the complexity of their linguistic input when speaking to language beginners. We show here that such active learning mechanism can improve considerably the speed of language formation in Naming Game models. We compare two scenarios for the interactions: either the speaker exherts an active control, or the hearer does. The latter scenario shows faster dynamics, with more robustness.

Motivations

. However, it still doesn't scale well to complex meaning spaces and a large number of possible word-meaning associations, implying high competition among lexical conventions.

.

Naming Games are a computational framework, elaborated to simulate the

self-organization of lexical conventions in the form of a multi-agent model [START_REF] Steels | Language games for autonomous robots[END_REF]. Through repeated local interactions between random couples of agents (designated speaker and hearer), shared conventions emerge. Interactions consist of uttering a word -or an abstract signal -refering to a topic, and evaluating communication success or failure.

However, a lot of processes involved in these interactions are random choices, especially the choice of a communication topic. Some preliminary work on the introduction of active learning algorithms in these models already shows significant improvement of the convergence process towards a shared vocabulary, but only with the speaker actively controlling vocabulary growth [START_REF] Oudeyer | Developmental exploration in the cultural evolution of lexical conventions[END_REF][START_REF] Schueller | Active Learning Strategies and Active Control of Complexity Growth in Naming Games[END_REF][START_REF] Cornudella | How intrinsic motivation can speed up language emergence[END_REF] .

Memorization skills of infants are improved through active query of lexical knowledge [START_REF] Partridge | Young Childrens Self-Directed Information Gathering on Touchscreens[END_REF], and experiments with children learning tasks in a social context suggest that this active behavior may also be part of the mechanisms used naturally in an interacting population of human learners [START_REF] Vredenburgh | Young children's help-seeking as active information gathering[END_REF]. In this work, we adapt the existing algorithms to a variant of the Naming Games where the hearer is actively controlling the complexity growth of the shared lexicon.

Methods

Interactions

The exact interaction process used in this work can be described as follows: Among the population, two agents are randomly picked and designated as speaker and hearer. A topic is chosen within the set of possible meanings (either randomly or actively by one of the agents), and the speaker utters the word associated (in its own vocabulary) to this meaning. The hearer then guesses the meaning of the word, and compares it to the actual topic. If the two meanings match, the interaction is successful. Otherwise, the communication is a failure, and both agents have the opportunity to update their vocabularies. We distinguish here three different scenarios: the topic is chosen randomly (like in the original models), either by the speaker, or the hearer (figure 1).

Vocabularies are represented as binary matrices, rows and columns being respectively meanings and words. The sets of meanings M and words W are finite (cardinalities M and W ) and symbolic (no grounded meaning or word). All N agents of the considered population start with empty vocabularies (all-0 matrices).

In [START_REF] Wellens | Adaptive Strategies in the Emergence of Lexical Systems[END_REF], a classification of Naming Games interaction types is proposed. We will employ one of the vocabulary update method described there, the Imitation Strategy (figure 2, choice explained in section 4). An agent always adds to its vocabulary the meaning-word association used in the interaction, erasing all potential synonyms or homonyms. This ensures a maximum of one associated word per meaning.

5.

Hearer's choice 1.

Figure 1. Interaction process for both active scenarios considered in this work. Beforehand, two individuals have been randomly selected among a population, an designated as speaker (S) and hearer (H). Speaker's choice: 1. S chooses a topic, 2. S checks its vocabulary to find or invent an associated word, 3. S utters the word, 4. H guesses the intended meaning, 5. S indicates the intended meaning. Hearer's choice: 1. H chooses a topic, 2. H indicates the intended meaning, 3. S checks its vocabulary to find or invent an associated word, 4. S utters the word, 5. H checks its vocabulary for a meaning associated to the uttered word. In both cases, if all meanings match, the interaction is considered a success, otherwise a failure. After the process, both agents can update their vocabularies.

0 0 0 0 0 0 1 0 kasof rimi karak potaf kasof rimi karak potaf 0 1 0 0 0 0 0 1 Speaker Hearer SUCCESS 0 1 0 0 0 0 1 0 kasof rimi karak potaf kasof rimi karak potaf 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 kasof rimi karak potaf kasof rimi karak potaf Speaker Hearer 0 1 0 0 0 0 1 0 kasof rimi karak potaf kasof rimi karak potaf 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 FAILURE Figure 2.
Vocabulary update policy: in the two interactions described here, topic was the object apple, and uttered word was rimi. When the speaker is refering to an unlabeled (for him) meaning, it creates an association during the update. When an interaction fails, the hearer adds the used association, and erases any conflicting homonym or synonym.

Strategies for the active choice of topics

During an interaction (figure 1), one of the involved agents (speaker or hearer) chooses the topic, i.e. the meaning infered by the speaker. This choice is done with only local information; in other words an agent doesn't access the memory of the others. The way of picking of the topic is called a strategy. A uniform random choice over the set of meanings is called the naive strategy.

The three active strategies used in this work (figure 3) cover a certain diversity of active mechanisms. This work analyzed 3 different strategies, each of which balances two types of behaviors: choosing an unlabeled meaning (to increase the vocabulary size) or choosing an already labeled one (in order to consolidate the existing vocabulary). In the latter case, the agent employs a confidence function to discriminate among known meanings those which would benefit consolidation in priority.

Naive (random)

Success Threshold

Minimal counts Info. Gain softmax

m ←random(M) if mean succ(i) succ(i)+f ail(i) i∈LM ≥ α α α: m ←random(UM) else: m ←argmini∈LM succ(i) succ(i)+f ail(i) if ∀i ∈ LM succ(i) > n n n: m ←random(UM) else: m ←argmini∈LM(succ(i)) if Pexp(µ,β β β)≥ random([0, 1]): m ←random(UM) else: m ←random(LM)
M: all meanings, LM: labeled meanings, UM: unlabeled meanings, µ: vocabulary size (# word-meaning associations) succ: # successful interactions per meaning, f ail: # failed interactions per meaning, Pexp: equations 1, 2 and tabular 4 Figure 3. Strategies: Choice of meaning m (by speaker or hearer, depending on the scenario) Speaker:

Pexp(µ, β) = e G(+1)•p(+1) β e G(+1)•p(+1) β + e G(-1)•p(-1) β (1)
Hearer:

Pexp(µ, β) = e - G(-1)•p(-1) β e G(+1)•p(+1) β + e - G(-1)•p(-1) β (2) Speaker's choice ∆µ Info. Gain G µ (bits) Probability p µ +1 log 2 (W -µ) (M -µ)(W -µ) M W 0 0 µ(1+M +W -2µ) M W -1 -log 2 (W -µ + 1) µ 2 -µ M W Hearer's choice ∆µ Info. Gain G µ (bits) Probability p µ +1 (explore) log 2 (W -µ) W -µ W 0 (explore) 0 µ W 0 (check) 0 W -µ+1 W -1 (check) -log 2 (W -µ + 1) µ-1 W Figure 4
. In both studied scenarios, hearer's possible outcomes with associated gains and probabilities, when having µ associations in the vocabulary V. There are M meanings and W words. To determine the probabilities, each agent assumes the other agent's vocabulary to be a permutation of its own or in other words, that they share the same µ value. Information measure (defining the gain) is introduced in section 2.3.

Success threshold

The Success Threshold strategy was first introduced in Oudeyer and Delaunay (2008). The confidence function is the percentage of successful interactions, computed for each meaning. If its average value exceeds a given threshold (parameter α), the agent will choose an unlabeled meaning. Otherwise, the meaning with the lowest confidence value is chosen (randomly in the original definition).

Minimal count of successes

This strategy is defined by a confidence function being the sum of successful interactions, per meaning. If it is higher than a given minimum value (parameter n) for all labeled meanings, the agent will choose an unlabeled meaning. Otherwise, the meaning with the lowest value is chosen.

Information gain soft maximization

This strategy is an extension of the Information Gain maximization strategy introduced in [START_REF] Schueller | Active Learning Strategies and Active Control of Complexity Growth in Naming Games[END_REF]. The agent chooses between exploring or teaching/checking depending on the expected outcome of the interaction in each case, always for the hearer, quantified as information gain (see 2.3). The choice is done according to a soft-maximization probability distribution (parameter β).

Those expected values are computed following a simple assumption: the other agent involved in the interaction has a vocabulary of the same size, but completely independant. In other words, it is supposed to be a random permutation of the binary matrix representing the vocabulary of the decision-making agent. A speaker, if making a decision, will prefer to maximize its outcome for each given hearer.

A hearer on the other hand will prefer to check, while minimizing the information loss and avoiding early exploration (see equations 1 & 2).

Measure

To compare the strategies, we will use the measure introduced in Schueller and [START_REF] Schueller | Active Learning Strategies and Active Control of Complexity Growth in Naming Games[END_REF]. It describes convergence towards a state where all agents have an identical vocabulary, hence sharing a common lexicon. Computed over a population of agents, it takes values in range [0, 1]. When the measure equals 0, no agent shares any word-meaning association with any other (maximum distance to converged state), whereas a value of 1 means the population has converged. The exact definition of the measure is the normalized quantity of shared information between 2 agents' vocabularies, averaged over all possible couples in the population. All strategies used, including the naive one, do converge in finite time. The only constraint is M≤W. A proof can be found in [START_REF] Schueller | Active Learning Strategies and Active Control of Complexity Growth in Naming Games[END_REF].

Results

Parameters

To set the parameter of the strategies, we will use the method introduced in Schueller and [START_REF] Schueller | Active Learning Strategies and Active Control of Complexity Growth in Naming Games[END_REF] and retain values yielding fast convergence dynamics. We ran simulations for different values of the parameter spanning the possible interval, and plotted a snapshot of the status of all simulations after a given number of interactions. We then inferred a best value for the parameter, while having an idea of the robustness of this choice. In all the simulations of this paper, we used M=W=N=20. In all cases we found parameter values yielding convergence in less than 10.000 interactions. According to figure 5, for speaker's choice: α = 85%, n = 20 and β = 0.2; for hearer's choice: α = 85%, n = 0 and β = 0.35. The flatness of the curves indicates that hearer's choice parameters are more robust to a change in value than in the speaker's choice scenario. . Convergence speed dependance on strategy parameters, for 3 strategies and 2 active interaction scenarios (see section 3.1, strategies described in section 2.2). In all cases hearer's choice scenario parameters are more robust to change in value. Snapshots are taken for concurrent strategies spanning a relevant parameter interval, at different time steps (500,1000,2000,5000,10.000 interactions). Vertical lines show parameter values chosen for the comparisons in figure 6. (M=W=N=20, averaged over 8 trials) Figure 6. Strategy comparisons in both active scenarios. Naive strategy converges slowly (after 1.000.000 interactions -not depicted here). Hearer's choice policy is more efficient for all active learning strategies. Last 5% of information are acquired slower when the speaker is choosing. Vertical lines show full convergence time for strategy. (M=W=N=20, averaged over 8 trials)

Comparison

As we can see in figure 6, active learning strategies were shown to speed up convergence significantly, for all tested strategies. Convergence to a shared lexicon over the whole population was reached between 4000 and 10.000 interactions, which was a significant improvement compared to the naive strategy (converging after 10 6 interactions).

In the hearer's choice scenario, convergence process is faster for all active learning strategies. However the success threshold strategy shows similar dynamics in both scenarios for the first 95% of normalized information. The last 5%, for all strategies, are slowly acquired when the speaker is choosing, compared to the dynamics of the first 95%. When the hearer is choosing, the dynamics of the last 5% stays comparable to the overall dynamics. Information gain maximization is slower than the other two strategies. Minimal counts strategy shows a shift in convergence speed when speaker is choosing and the set count value is reached. When the hearer is choosing, it performs as well as the success threshold strategy.

Discussion

It is important to understand that our research was carried out with the assumption of Imitation Strategy. Other possible vocabulary update policies, including "Minimal" and "Lateral Inhibition" (see [START_REF] Wellens | Adaptive Strategies in the Emergence of Lexical Systems[END_REF]), are likely to exhibit different convergence rates. The information measure from [START_REF] Schueller | Active Learning Strategies and Active Control of Complexity Growth in Naming Games[END_REF] is not yet defined for those cases, but an extension of the study including those alternative vocabulary update policies is planned.

In this work, we have shown that active information request by the hearer can be a more efficient policy than active information provision by the speaker, in the Naming Games framework. The observed difference between the two policies lies mostly in the acquisition dynamics of the last 5% of information. These findings support that high correlation between vocabularies is best handled active learning than by active teaching.

Furthermore, active information request is more robust, as a wide range of parameters lead to improved dynamics (compared to random choice), for all studied algorithms. On the other hand, the parameters need to be finely tuned for the speaker's choice policy. This could be understood as a difference in required skill-level between policies. From an evolutionary point of view, it implies that an active information request behavior may be developped faster.

If the dependance of the results on the numbers of meanings, words and agents first needs to be studied, the next logical step would be to mix both policies, by giving agents the opportunity to take turns. Deciding speakers may bring faster dynamics at the beginning, and hearers near the end of the lexicon establishment process. An active choice of the partner, like humans selecting who they may prefer interacting with, could also bring some further improvement.

Source code

The code used for the simulations of this paper was written in Python. It is available as open source software, along with explanatory notebooks, on the Inria Flowers team github: https://github.com/flowersteam/naminggamesal

  Figure5. Convergence speed dependance on strategy parameters, for 3 strategies and 2 active interaction scenarios (see section 3.1, strategies described in section 2.2). In all cases hearer's choice scenario parameters are more robust to change in value. Snapshots are taken for concurrent strategies spanning a relevant parameter interval, at different time steps(500, 1000, 2000, 5000, 10.000 interactions). Vertical lines show parameter values chosen for the comparisons in figure6. (M=W=N=20, averaged over 8 trials)

Acknowledgements

We would like to thank all those who provided help and feedback, especially Fabien Benureau, Sébastien Forestier, Florian Golemo and Alexandre Spriet. This research was partially funded by ERC Grant EXPLORERS 240007.


Christian Boitet

Valérie Bellynck

NGUYEN Achille Falaise

Valerie Boitet

Achille Bellynck

Hong-Thai Falaise

Nguyen

Operationalization of interactive Multilingual Access Gateways (iMAGs) in the Traouiero project French version included, obtained by MT + postediting with an iMAG, demo possible

Keywords: Interactive translation gateway, iMAG, MT post-editing, collaborative translation Presentation

We will explain and demonstrate iMAGs (interactive Multilingual Access Gateways), in particular on a scientific laboratory web site and on the Greater Grenoble (La Métro) web site. This bilingual presentation has been obtained using an iMAG.

This presentation is an adaptation and update of an article presented as a demonstration only to TALN-2010. The names of the files have been kept the same, although their contents are slightly different. The iMAG concept has been proposed by Ch. Boitet and V. Bellynck in 2006(Boitet & al. 2008[START_REF] Boitet | Main research issues in building web services for mutualized, non-commercial translation[END_REF], and reached prototype status in November 2008, with a first demonstration on the LIG laboratory Web site. It has been adapted to the DSR (Digital Silk Road) Web site in April 2009, and then to more than 50 other Web sites. These first prototypes are extensions of the SECTra_w [START_REF] Huynh | SECTra_w : an Online Collaborative System for Evaluating, Post-editing and Presenting MT Translation Corpora[END_REF]) online translation corpora support system. Since the beginning of 2011, we are operationalizing this software with a view to deploy it as a multilingual access infrastructure, in the context of the French ANR (National Agency for Research) Traouiero "emergence" project. An iMAG is an interactive Multilingual Access Gateway very much like Google Translate at first sight: one gives it a URL (starting Web site) and an access language and then navigates in that access language. When the cursor hovers over a segment (usually a sentence or a title), a palette shows the source segment and proposes to contribute by correcting the target segment, in effect post-editing an MT result. With Google Translate, the page does not change after contribution, and if another page contains the same segment, its translation is still the rough MT result, not the polished post-edited version. The more recent Google Translation Toolkit enables one to MT-translate and then post-edit online full Web pages from sites such as Wikipedia, but again the corrected segments don't appear when one later browses the Wikipedia page in the access language. By contrast, an iMAG is dedicated to an elected Web site, or rather to the elected sublanguage defined by one or more URLs and their textual content. It contains a translation memory (TM) and a specific, preterminological dictionary (pTD), both dedicated to the elected sublanguage. Segments are pretranslated not by a unique MT system, but by a (selectable) set of MT systems. Systran and Google are mainly used now, but specialized systems developed from the postedited part of the TM, and based on Moses, will be also used in the future. The powerful online contributive platforms SECTra_w and PIVAX [START_REF] Nguyen | PIVAX, an online contributive lexical database for heterogeneous MT systems using a lexical pivot[END_REF]) are used to support the TMs and pTDs. Translated pages are built with the best segment translations available so far. While reading a translated page, it is possible not only to contribute to the segment under the cursor, but also to seamlessly switch to SECTra_w online post-editing environment, equipped with proactive dictionary help and good filtering and search-and-replace functions, and then back to the reading context. A translation relay is being implemented to define the iMAGs or other translation gateways used by an elected Web site, select and parameterize the MT systems and translation routes used for various language pairs, and manage users, groups, projects (some contributions may be organized, other opportunistic), and access rights. Finally, MT systems tailored to the selected sublanguage can be built (by combinations of empirical and expert methods) from the TM and the pTD dedicated to a given elected Web site. That approach will inherently raise the linguistic and terminological quality of the MT results, hopefully converting them from rough into raw translations. The demonstration will use some iMAGs created by the AXiMAG startup for various Web sites, such as those of the LIG lab (http://service.aximag.fr:8180/xwiki/bin/view/imag/liglab) and of La Metro (Greater Grenoble) web site (http://service.aximag.fr:8180/xwiki/bin/view/imag/lametro), where access in Chinese and English was enabled in 2010 for the Shanghai Expo.

In this written presentation, we will apply the iMAG technique to the presentation itself. It was first written in English under Word, in file TALN-2010-Demo-iMAG-v2_en.rtf. We then saved it in html and put the result (TALN-2010-Demo-iMAG-v2_en.htm) online (www-clips.imag.fr/geta/User/christian.boitet/iMAGs-tests/en).

We accessed it through the corresponding iMAG (http://service.aximag.fr:8180/xwiki/bin/view/imag/xan-en). Choosing French as access language, and tuning some parameters, we got the following view. Note the colored curly brackets enclosing the French segments. They appear on demand ("reliability" checkbox). Red indicates an MT output, green an postedition by a certified translator, and orange a postedition by somebody knowing the two languages to a certain level, and usually contributing on an occasional and volunteer basis. After postediting some segments directly on the Web page, we obtain the following state (notice some green brackets). Having logged on as a "certified" contributor, we could continue in "advanced mode", switching to the SECTra_w online postediting environment, under which postediting is much faster (specialized lexical help is not available until the corpus of postedited segments is large enough, so that it is not shown here). After having postedited the whole presentation, we extracted the current French version from SECtra_w. After a simple manipulation (to be automated in the future), the text was obtained in a bilingual table :   By contrast, an iMAG is dedicated to an elected Web site, or rather to the elected sublanguage defined by one or more URLs and their textual content.

En revanche, une IMAG est dédiée à un site Web élu, ou plutôt au sous-langage élu défini par une ou plusieurs URL et leur contenu textuel. It contains a translation memory (TM) and a specific, preterminological dictionary (pTD), both dedicated to the elected sublanguage.

Elle contient une mémoire de traductions (MT) et un dictionnaire spécifique preterminologique (pTD), les deux dédiés au sous-langage élu. Segments are pretranslated not by a unique MT system, but by a (selectable) set of MT systems. Systran and Google are mainly used now, but specialized systems developed from the postedited part of the TM will be also used in the future.

Les segments sont prétraduits non pas par un système de TA unique, mais par un ensemble (sélectionnable) de systèmes de TA. Systran et Google sont principalement utilisés aujourd'hui, mais des systèmes spécialisés développés à partir de la MT postéditée seront également utilisés dans l'avenir. (Boitet & al. 2008[START_REF] Boitet | Main research issues in building web services for mutualized, non-commercial translation[END_REF], et a atteint l'état de prototype en novembre 2008, avec une première démonstration sur le site Web du laboratoire LIG. Il a été adapté au site Web DSR (Digital Silk Road) en avril 2009, puis à plus de 50 autres sites Web. Ces premiers prototypes sont des extensions du système SECTra_w [START_REF] Huynh | SECTra_w : an Online Collaborative System for Evaluating, Post-editing and Presenting MT Translation Corpora[END_REF]) de support en ligne de corpus de traductions. Depuis début 2011, nous sommes en train d'opérationnaliser the logiciel en vue de le déployer comme une infrastructure d'accès multilingue, dans le contexte du projet "emergence" Traouiero de l'ANR (Agence Nationale de la Recherche). Une iMAG est une passerelle interactive d'accès multilingue (interactive Multilingual Access Gateway), ressemblant beaucoup à Google Translate, à première vue: on donne une URL (site Web de départ) et une langue d'accès, et on navigue ensuite dans cette langue d'accès. Lorsque le curseur passe sur un segment (le plus souvent une phrase ou un titre), une palette montre le segment source et propose de contribuer en corrigeant le segment cible, en fait en post-éditant un résultat de TA. Avec Google Translate, la page ne change pas après la contribution, et si une autre page contient le même segment, sa traduction est toujours le résultat de TA grossière, pas la version polie post-éditée. La boîte à outils de traduction plus récente Google Translation Toolkit permet de traduire par TA et ensuite de postéditer en ligne des pages Web complètes tirées de sites tels que Wikipedia, mais de nouveau, les segments corrigés n'apparaissent pas quand on regarde plus tard la page de Wikipedia dans la langue d'accès. En revanche, une IMAG est dédiée à un site Web élu, ou plutôt au sous-langage élu défini par une ou plusieurs URL et leur contenu textuel. Elle contient une mémoire de traductions (MT) et un dictionnaire spécifique preterminologique (pTD), les deux dédiés au sous-langage élu. Les segments sont prétraduits non pas par un système de TA unique, mais par un ensemble (sélectionnable) de systèmes de TA. Systran et Google sont principalement utilisés aujourd'hui, mais des systèmes spécialisés développés à partir de la MT postéditée, et bases sur Moses, seront également utilisés dans l'avenir. Les plates-formes contributives puissantes SECTra_w et PIVAX [START_REF] Nguyen | PIVAX, an online contributive lexical database for heterogeneous MT systems using a lexical pivot[END_REF]) sont utilisées pour supporter les MT et les pTD. Les pages traduites sont construites avec les meilleures traductions des segments disponibles à ce jour. Pendant la lecture d'une page traduite, il est possible non seulement de contribuer au segment sous le curseur, mais aussi de passer de façon transparente sous l'environnement de postédition en ligne de SECTra_w, muni d'une aide dictionnairique proactive et de bonnes fonctions de filtrage et de rechercheremplacement, et ensuite de revenir dans le contexte de lecture. Un relais de traduction est en cours d'implémentation pour définir les iMAG ou d'autres passerelles de traduction utilisées par un site Web élu, pour sélectionner et paramétrer les systèmes de TA et les itinéraires utilisés pour la traduction des différentes paires de langues, et pour gérer les utilisateurs, les groupes, les projets (certaines contributions peuvent être organisées, d'autres opportunistes), et les droits d'accès. Enfin, des systèmes de TA faits sur mesure pour le sous-langage sélectionné peuvent être construits (par la combinaison de méthodes empiriques et expertes) à partir de la MT et la du pTD dédiés à un site Web élu donné. Cette approche accroîtra intrinsèquement la qualité linguistique et terminologique des résultats de TA, en les transformant de traductions grossières en traductions brutes. La démonstration utilisera des iMAG créées par la jeune pousse AXiMAG pour divers sites Web, tels que ceux du laboratoire LIG (http://service.aximag.fr:8180/xwiki/bin/view/imag/liglab) et du site Web de La Métro (agglomération grenobloise) (http://service.aximag.fr:8180/xwiki/bin/view/imag/lametro), où l'accès en chinois et en anglais a été activé en 2010 pour l'Expo de Shanghai. Dans cette présentation écrite, nous allons appliquer la technique iMAG à la présentation elle-même. Elle a d'abord été écrite en anglais sous Word, dans le fichier TALN-2010-Demo-IMAG-v2_en.rtf. Nous l'avons ensuite sauvée en format html, et avons mis le résultat (TALN-2010-Demo-IMAG-v2_en.htm) en ligne (wwwclips.imag.fr/geta/User/christian.boitet/iMAGs-tests/en). Nous y avons accédé par l'intermédiaire de l'iMAG correspondante (http://service.aximag.fr:8180/xwiki/bin/view/imag/xan-en). Après avoir choisi le français comme langue d'accès, et réglé certains paramètres, nous avons obtenu la vue suivante -voir Figure 1. Notez les accolades de couleur entourant les segments français. Elles apparaissent à la demande (case à cocher "fiabilité"). Le rouge indique une sortie de TA, le vert une postédition par un traducteur certifié, et l'orange une postédition par quelqu'un connaissant les deux langues à un certain niveau, et de contribuant généralement de façon occasionnelle et à titre bénévole. Après avoir postédité certains segments directement sur la page Web, on obtient l'état suivant (notez certains crochets verts) -voir Figure 2. Après nous être connecté en tant que contributeur "certifié", nous avons pu continuer en mode "avancé", en passant sous l'environnement de postédition en ligne de SECTra_w, sous lequel la postédition est beaucoup plus rapide (l'aide lexicale spécialisée n'est pas disponible jusqu'à ce que le corpus de segments postedités soit assez grand, donc elle n'est pas montrée ici) -voir Figure 3. Après avoir postédité toute la présentation, nous avons extrait la version française courante de SECtra_w,. Après une manipulation simple (qui sera automatisée dans le futur), le texte a été dans un tableau bilingue : By contrast, an iMAG is dedicated to an elected Web site, or rather to the elected sublanguage defined by one or more URLs and their textual content.

En revanche, une IMAG est dédiée à un site Web élu, ou plutôt au sous-langage élu défini par une ou plusieurs URL et leur contenu textuel. It contains a translation memory (TM) and a specific, preterminological dictionary (pTD), both dedicated to the elected sublanguage.

Elle contient une mémoire de traductions (MT) et un dictionnaire spécifique preterminologique (pTD), les deux dédiés au sous-langage élu. Segments are pretranslated not by a unique MT system, but by a (selectable) set of MT systems. Systran and Google are mainly used now, but specialized systems developed from the postedited part of the TM will be also used in the future.

Les segments sont prétraduits non pas par un système de TA unique, mais par un ensemble (sélectionnable) de systèmes de TA. Systran et Google sont principalement utilisés aujourd'hui, mais des systèmes spécialisés développés à partir de la MT postéditée seront également utilisés dans l'avenir.

Figure 1 :

 1 Figure 1 : screen of of iMAG after MT, parallel presentationécran d'une iMAG après TA, présentation parallèle

Figure 2 :

 2 Figure 2 : screen of an iMAG after direct postediting of some segments on a Web pageécran d'une iMAG après postédition directe de quelques segments sur une page Web

Figure 3 :

 3 Figure 3 : screen of an iMAG after indirect postediting under SECTra_w of other segmentsécran d'une iMAG après postédition indirecte sous SECTra_w d'autres segments

Figure 4 :

 4 Figure 4 : excerpt of a bilingual aligned text extracted from an iMAG-translated target Web pageextrait d'un text bilingue aligné provenant d'une page Web traduite par une iMAGAfter 15 more minutes of formatting, we got the French text that follows. References are factorized after the French version as the custom is not to translate them when translating between English and French.

Figure 5 :

 5 Figure 5 : partie d'un texte bilingue aligné extrait d'une page Web cible traduite par une iMAG Les références ont été factorisées après la version en français car la coutume est de ne pas traduire les références entre anglais et français.

Appendix: screenshots of two other iMAGs / images d'écran de deux autres iMAG

Short CV (Christian Boitet)

Ch. Boitet is professor of computer science at Université Joseph Fourier (Grenoble 1), where he has taught algorithmics, compiler construction, formal languages & automata, elementary logic, formal systems, and natural language processing. He is one of the authors of Ariane-G5, GETA's generator of MT systems. He has presented communications in many national and international conferences and published in various journals and books. He has also edited a book dedicated to the presentation of Pr. Vauquois' scientific work, as well as several international conference proceedings. He has been in charge of several research contracts aiming at reaching the operational stage and the industrial stage. He has also been involved in and/or in charge of GETA's participation in several cooperative research efforts. He is a member of ICCL and has been one of the organizers of COLING-92. In 1998, he was Programme Chair of COLING-ACL'98. He is and has been a regular reviewer for several journals and conferences, and has been in the programme committees of many congresses. His current interests include personal dialogue-based MT for monolingual authors (GETA's LIDIA project, international UNL project), speech translation (CSTAR project), machine helps to translators and interpreters, integration of speech processing inspired techniques in MT, multilingual lexical data bases (Papillon project), and specialized languages and environments for lingware engineering and linguistic research (Ariane-Y project).

He introduced several innovations in various aspects of MT, such as multitarget pidgin translation with limited reordering (Russian to French & English, TAUM, 1972), contextual rewriting rules in transformational systems for decorated trees (ROBRA, COLING-78), vertical parallelism and guarded recursion and iteration in ROBRA, a formalization of structured string-tree correspondences (SSTCs, with Zarin Y., COLING-88) later used as the basis of the SiSTeC-ebmt systems (USM & MMU, Malaysia), the whiteboard architecture for heterogeneous speech translation systems (with M. Seligman, 1993), the use of text processors as pseudo-syntactic editors to develop multitarget dictionaries (French-English-Malay or FEM dictionary, 1996), the LIDIA architecture based on Interactive Disambiguation by Auhors and the concept of self-explaining documents (SED, with H. Blanchon, MT15YO, 1994), the concept of metaenvironment for MT, the definition of heterogeneous MT systems (with Nguyen H.T., 2009), several concepts to enable rigorous processing of aligned translation corpora (with Huynh C.P., 2010), such as multilingualized segments, pseudodocuments, metadocuments, multiple and recursive segmentation, and techniques to implement incrementally modifiable MT systems for unlimited translation units (with JC. Durand, 2011). More general contributions are the distinctions between linguistic, computational and operational architectures of MT systems, and between strong and weak translation problems (with A. Malik, 2010).


M Sghiar
email: msghiar21@gmail.com

Keywords: Graph, Hamilton cycles, P=NP

Atomic algorithm and the servers's use to find the Hamiltonian cycles

M Sghiar

Introduction

It is known both theoretically and computationally so difficult to find a Hamilton cycles(paths) in simple graphs, and that this prblem is a classical NP Complete problem. (see [START_REF] Du | A polynomial time algorithm for hamilton cycle[END_REF] [2] [START_REF] Johnson | Computers and intractability :a guid to the theory of np-completeness[END_REF] and [START_REF] Diestel | Graph theory[END_REF] ).

Inspired by the movement of the particles in the atom, I demonstrated in [START_REF] Sghiar | Algorithmes quantiques, cycles hamiltoniens et la k-coloration des graphes[END_REF] the exsitence of a ploynomial algorithm of the order O(n 3 ) for finding Hamiltonian cycles in a graph. In this article I will give an improvement in space and in time of the algorithm says : we know that there exist several methods to find the Hamiltonian cycles like the Monte Carlo method, Dynamic programming, or DNA computing.

Unfortunately they are either expensive or slow to execute it. Hence the idea to use multiple servers to solve this problem.

The first code is faster but consumes more memory, while the second, although slower, but uses less memory since it uses writing to files during the search of Hamilton cycles.

These two algorithms give the idea of the servers' use. for j in range (n ): for j in range (n ): Each point x i on the graph will be considered as a server, and each server x i sends F(j,T) -T is in d[i]-to each server x j with which it is connected . And finally the server x 0 will receive and display the Hamiltonian cycles if they exist.

The First code

G[ i ][ j ]=1 #G [i ][ j ]= input () # print " G[ " ,i , " ][ " ,j , "] " #G [i ][ j ]= input () # if i <= j : #G [i ][ j ]= random . randint (0 ,1) # else : G[ i ][ j ]= G[ j ][ i ] # print " G[ " ,i , " ][ " ,j ," ]= " ,G[ i ][ j] d ={} d [0]=[[ n ,0]] for j in range (n ): if G [0][ j ]!=0 and 0!= j : d[ j ]=[[ n -1 ,j ,0]] d [0]=[] # print d [j ] else : d[ j ]=[[0 , j ]] # print d [ j] L =[] H =[]
G[ i ][ j ]=1 #G [i ][ j ]= input () # print " G[ " ,i , " ][ " ,j , "] " #G [i ][ j ]= input () # if i <= j : #G [i ][ j ]= random . randint (0 ,1) # else : G[ i ][ j ]= G[ j ][ i ] # print " G[ " ,i , " ][ " ,j ," ]= " ,G[ i ][ j] d ={} f ={} for i in range (n ): f [i ]=
Obviously the servers can work simultaneously, which speeds up the execution of the program and solves the problem of full memory.

  from scipy import * import numpy as np import random import time start = time . time () # The Feynman code in Python : # Written by sghiar 24 may , 2016 was 21:25 p. m .. # This code allows you to find the Hamilton cycle if it exists . . eye (n ,n ) # for i in range (n ):

#

  for k in range (0 , n **2) : if len ( H) != 0 : print H print ( " Time :" , time . time () -start ) break print (k , " Time :" , time . time () -start ) print " The program is looking for the Hamiltonien cycles ... " if k %n ==0: for T in d [k %n ] : if T [0] == 0 : H. append (T ) else : pass del d [0] d [0]=[] elif k %n !=0: for T in d [k %n ] : for j in range (0 , n) : if T [0] <=0 or (j in R (T ) and j !=0) : pass else : if G[ k% n ][ j ]!=0 and (k %n ) != j : d[ j ]+=[ F (j ,T ) ] else : pass del d[ k% n] d[ k% n ]=[] # Hamiltonians Cycles : if len ( H ) !=0: for elt in H : print (" There exist the Hamiltonian cycles " ) print ( R( elt ) ," Is one Hamiltonien cycle , Its distance is :" , D ( elt ) ) else : print ( " No Hamiltonian cycles ") # End of code3 The second code And if we want to use less memory ram , we can use this algorithme : / usr / bin / env python # coding = utf -8 import decimal from scipy import * import numpy as np import random import time start = time . time () import os # The Feynman code in Python : # Written by sghiar 28 may , 2016 mai 14:53 p. m .. # This code allows you to find the Hamiltonian cycle if it exists . We construct the graph G : print (" number of cities =" ) n = input () G = np . eye (n ,n ) # for i in range (n ):

  file ( " fichier_ % d . txt "%i , " w" ) f [0]. write ( str ([n ,0]) + "\ n" ) for j in range (1 , n ): if G [0][ j ]!=0 and 0!= j : f[ j ]= file ( " fichier_ %d . txt "%j , " a" ) f[ j ]. write ( str ([n -1 ,j ,0]) +" \n ") else : f[ j ]. write ( str ([0 , j ]) +" \n ") , " Time :" , time . time () -start ) print " The program is looking for the Hamiltonien cycles ... " if k% n ==0: #f [k %n ]= file ( " fichier_ % d. txt " %( k% n) , "r ") f[ k% n ]= open ( " fichier_ %d . txt " %( k %n ) , " r" ) for T in f [k %n ] : = file ( " fichier_ %d . txt " %( k %n ) , " a" ) # os . remove (" fichier_0 . txt ") elif k %n !=0: f[ k% n ]= open ( " fichier_ %d . txt " %( k %n ) , " r" ) for T in f [k %n ]: T. split ( '= ') exec ( 'T= '+T) x= T for j in range (0 , n) : if x [0] <=0 or ( j in R (x ) and j !=0) : pass else : if G[ k% n ][ j ]!=0 and (k %n ) != j : f[ j ]= file ( " fichier_ % d. txt " % j , "a ") f[ j ]. write ( str ( F(j , x) )+ "\ n" ) f[ j ]. close () else : pass del f [ k% n] # os . remove (" fichier_ %d . txt " %( k % n) ) f [k %n ]= file ( " fichier_ % d . txt " %( k %n ) , " a" ) # Hamiltonians Cycles : if len ( H ) !=0: for elt in H : print (" There exist the Hamiltonian cycles " ) print ( R( elt ) ," Is one Hamiltonien cycle , Its distance is :" , D ( elt ) ) else : print ( " No Hamiltonian cycles ") for i in range (n ): f [i ]. close () # End of code Note : The two algorithms above can be modified to use at least n servers to find the Hamiltonian cycles , so we will win time and space ( memory) : There exist several methods to find the Hamiltonian cycles such as the Monte Carlo method, Dynamic programming, or DNA computing. Unfortunately they are either expensive or slow to execute it. Hence the idea to use multiple servers to solve this problem.