document: the extinction curve describes how the extinction changes with the wavelength . 
   extinction is due to the presence of dust grains in the interstellar medium and its characteristics are different in a diffuse interstellar medium as compared to a dense interstellar medium . 
   thus , the knowledge of extinction curve is necessary to deredden magnitudes and colors of astronomical objects and to understand the physical properties of dust grains . 
   * hereafter ccm ) derived a mean extinction law ( for @xmath2 ) that depends on only one parameter @xmath3 . 
   they considered the sample used in the ultraviolet ( uv ) extinction study of @xcite based on _ international ultraviolet explorer _ extinction curves of 45 reddened milky way ob stars . 
   ccm searched for the corresponding optical and near - infrared ( ubvrijhkl ) photometry from the literature . 
   finally they used the intrinsic colors of @xcite for the appropriate spectral types to determine the extinction . 
   they obtained the following one - parameter family of curves that represents the uv to infrared ( ir ) extinction law in terms of @xmath0 :    @xmath4    where @xmath5 , and @xmath6 and @xmath7 are the wavelength - dependent coefficients . 
   equation ( [ ccmlaw ] ) is very powerful because it allows one to determine the extinction in some spectral region based on the extinction in a different spectral region , given that one knows @xmath0 . 
   the @xmath0 parameter ranges from about 2.0 to about 5.5 ( with a typical value of 3.1 ) when one goes from diffuse to dense interstellar medium . in this formalism 
   @xmath0 therefore characterizes the dust properties in the region that produces the extinction . 
   many authors used this parameter to study extinction , e.g. : @xcite searched for a relation between @xmath0 and other parameters that characterize the extinction curves ; @xcite studied the role of @xmath0 as the main regulatory agent of the penetration of radiation inside dark clouds ; @xcite discussed different methods to deredden the data and obtained a new estimate of the extinction law in terms of @xmath0 . 
   * hereafter gs ) applied a @xmath8 minimization to compute the @xmath0 values for a sample of stars with uv extinction data using the linear relation ( [ ccmlaw ] ) . a similar method with weights 
   was used by @xcite to determine @xmath0 and @xmath1 toward a sample of stars with known color excesses in ubvrijhkl .    here 
   we extend the method used by gs in order to obtain improved @xmath0 values for the lines of sight toward a sample of stars with known extinction data in the uv . 
   the structure of this paper is the following . in  [ th.cons . 
   ] we discuss the theoretical basis of our @xmath0 derivation . in  [ data ] 
   we describe our data sources . 
   we present the results and assess the consistency between different samples and theoretical approaches in  [ results ] . finally in  [ conclusion ] 
   we discuss our results and comment on the future work . 
   the interstellar dust grains span a wide range of sizes from a few angstroms to a few micrometers . in general 
   , they reduce the intensity of the transmitted beam by two physical processes : absorption and scattering . 
   the extinction is the result of these two processes . 
   + the apparent magnitude @xmath9 of each star as a function of wavelength may be written as @xmath10 @xmath11 where @xmath12 , @xmath13 and @xmath14 represent absolute magnitude , distance and total extinction , respectively , and subscripts `` red '' and `` comp '' denote reddened and comparison stars , respectively . 
   the extinction as a function of @xmath15 may be obtained by comparing corresponding stars paired according to spectral properties . in principle , the comparison star should be of the same spectral classification as the reddened star , but with a negligible extinction . 
   if the reddened star and the comparison star have the same spectral classification it also means that they have very similar intrinsic spectral energy distributions . 
   thus we have @xmath16 . 
   we also assume that @xmath17 . 
   the magnitude difference obtained from equation ( [ mred ] ) and ( [ mcomp ] ) is therefore :    @xmath18    the quantity @xmath19 is a constant term and may be eliminated by normalizing with respect to extinction difference in two standard wavelengths @xmath20 and @xmath21 :    @xmath22    generally , the extinction curves are normalized with respect to the b and v passbands in the @xcite system :    @xmath23    where @xmath24 , @xmath25 is the observed color and @xmath26 is the intrinsic color ( by construction equal to the color of the comparison star ) . 
   it is possible to obtain the absolute extinction by using the total to selective extinction ratio :    @xmath27    then :    @xmath28    ccm , for computational reasons , divided the complete extinction curve ( equation [ ccmlaw ] ) into three wavelengths regions and fitted the extinction law as a function of @xmath29 : +  infrared ( @xmath30 ) , +  optical / nir ( @xmath31 ) , +  ultraviolet and far - ultraviolet ( @xmath32 ) . 
   + for every wavelength , the coefficients @xmath6 and @xmath7 from equation ( [ ccmlaw ] ) are fixed and given by an appropriate expression . 
   observations from _ international ultraviolet explorer _ cover a range from @xmath33 ( @xmath15[@xmath34 = 1549 , 1799 , 2200 , 2493 , and 3294 ) , so we use the equations for the coefficients for the last two regions listed above . 
   for @xmath31 and @xmath35 we have : @xmath36 for @xmath37 : @xmath38+f_a(x);\\ & b(x)&=-3.090 + 1.825x+1.206/[(x-4.62)^2 + 0.263]+f_b(x).\end{aligned}\ ] ] where : @xmath39 @xmath40 @xcite compute @xmath0 values using equations ( [ ccmlaw ] ) and ( [ eps ] ) and by minimizing the quantity : @xmath41\}^2 \label{chi}\ ] ] the right side of equation ( [ chi ] ) is a second order polynomial ( parabola ) of @xmath0 with the minimum : @xmath42\right\}}{\sum_{i=1}^{n_{\rm bands}}(a(x_i)-1)^2 } \label{rvgs}\ ] ] + this formula is right when the errors in @xmath43 are identical for all bands . 
   our data ( see  [ data ] ) have errors that differ from band to band , so we use an improved @xmath8 , weighted by the observational errors . 
   ducati et al . 
   ( 2003 ) suggested the following @xmath8 for independent minimization with respect to @xmath0 and @xmath1 : @xmath44 ^ 2\ ] ] where @xmath45 are the weights associated with each band . 
   we use a related but different approach which stems from the fact that in addition to uv bands we also use @xmath46 as our input data . 
   we normalize our color excesses with @xmath46 to form @xmath47 . since @xmath48 , we minimize the following @xmath8 with respect to @xmath0 only : @xmath49 \ } ^2 ~e^2(b - v ) \label{chiweighted}\ ] ] setting @xmath50 and minimizing equation ( [ chiweighted ] ) with respect to @xmath0 we find : @xmath51/ \sigma_i^2 \}}{\sum_{i=1}^{n_{\rm bands } } \ { ( a(x_i)-1)^2/ \sigma_i^2 \ } } \label{rvweighted}\ ] ] where : @xmath52 \equiv   \left(\frac{\partial \epsilon(\lambda_i - v)}{\partial e(\lambda_i - v ) } \sigma[e(\lambda_i - v)]\right)^2 + \left ( \frac{\partial \epsilon(\lambda_i - v)}{\partial e(b - v ) } \sigma[e(b - v)]\right)^2   \nonumber\\ & = & \left(\frac{e(\lambda_i - v)}{e(b - v ) } \right)^2 \left [ \left ( \frac{\sigma[e(\lambda_i - v)]}{e(\lambda_i - v)}\right)^2 + \left(\frac{\sigma[e(b - v)]}{e(b - v ) } \right)^2 \right ] \label{sigma}\end{aligned}\ ] ] and @xmath53 & \equiv & \sigma^2[(m_{\lambda_i}-m_v ) - ( m_{\lambda_i}-m_v)_0 ] \nonumber\\ & = & \sigma^2[m_{\lambda_i}]+\sigma^2[m_v]+\sigma^2_{i,{\rm mismatch } } \label{sigmae}\end{aligned}\ ] ] the error terms on the right side of equation ( [ sigmae ] ) are described in table  [ table1 ] . in equation ( [ sigma ] ) 
   we assumed for simplicity that the errors in @xmath54 and @xmath46 are independent . 
   however , the values of @xmath47 and their errors for different bands are not independent . to get a good idea about the errors in @xmath0 we compute them in two ways . 
   first , we calculate the maximum error in @xmath0 , which is the straight sum of errors coming from different sources : @xmath55 then we obtain the error in quadrature which would properly describe total uncertainty if the errors from different sources were uncorrelated :    @xmath56 } \nonumber\\ & = & \frac{1}{\sum_{i=1}^{n_{\rm bands } } [ ( a(x_i)-1)^2/\sigma_i^2 ] } \cdot \sqrt { \sum_{j=1}^{n_{\rm bands } } \left ( \frac{a(x_j)-1}{\sigma_j } \right)^2 } \label{quaerr}\end{aligned}\ ] ]    neither description ( [ errabs ] ) nor ( [ quaerr ] ) is strictly correct : the real error in @xmath0 lies likely between these two estimates .    by definition :    @xmath57    where the second equality is a consequence of equation ( [ rvweighted ] ) . 
   therefore the maximum error in @xmath1 is given by } \frac{1}{\sigma_i^2 } = c$ ] , where @xmath58 is a constant . 
   from equation  ( [ sigma ] ) we conclude that this condition is in conflict with @xmath59=\sigma [ e(b - v)]$ ] assumed by gs . 
   if we ignore this conflict and force both conditions , then : @xmath60 \cdot \sigma [ e(b - v)]\ ] ] the results reported in table 1 of gs suggest that they used the above formula rather than their equation ( 8) . ] : @xmath61 + \left | \frac{\partial a_v } { \partial e(b - v ) } \right| \sigma [ e(b - v ) ] \nonumber\\ & = & \frac{1}{\sum_{i=1}^{n_{\rm bands } } ( a(x_i)-1)^2/\sigma_i^2 } \left [ \sum_{j=1}^{n_{\rm bands } } \left | \frac{(a(x_j)-1)}{\sigma_j^2}\right| \sigma[e(\lambda_j - v ) ] \right . \nonumber\\ & + & \left . 
   \left| \sum_{i=1}^{n_{\rm bands}}\frac{(a(x_i)-1)(-b(x_i))}{\sigma_i^2 } \right| \sigma[e(b - v ) ] \right ] \label{averrmax}\end{aligned}\ ] ] the error in quadrature is given by : @xmath62 + \left ( \frac{\partial a_v}{\partial e(b - v ) } \right ) ^2 \sigma^2[e(b - v ) ] } \nonumber\\ & = & \frac{1}{\sum_{i=1}^{n_{\rm bands } } ( a(x_i)-1)^2/\sigma_i^2 } \left[\left(\sum_{j=1}^{n_{\rm bands } } \frac{(a(x_j)-1)}{\sigma_j^2 } \right)^2\sigma^2[e(\lambda_j - v)]\right . 
   \nonumber\\ & + & \left.\left(\sum_{i=1}^{n_{\rm bands}}\frac{(a(x_i)-1)(-b(x_i))}{\sigma_i^2 } \right)^2\sigma^2[e(b - v ) ] \right]^{1/2 } \label{sigmaquadav}\end{aligned}\ ] ] 
   we use the data taken from the @xcite catalog of ultraviolet color excesses @xmath63 for stars of spectral types b7 and earlier . 
   the uv measurements are taken from _ astronomical netherlands satellite _ ( ans ) data @xcite and consist of observations in five uv channels with central wavelengths : @xmath64 1549 , 1799 , 2200 , 2493 , and 3294@xmath65 . 
   the sources of the data used to obtain @xmath66 as given by equation ( [ extinction ] ) and their errors are listed in table [ table1 ] . 
   we also consider another type of error : a ` mismatch error ' , which is caused by the fact that the reddened star and the comparison star may have slightly different colors . 
   @xcite give in their section 2c , table 1b ultraviolet color excess errors which include errors associated with spectral type misclassification ( mismatch error ) and errors in the intrinsic colors . 
   we adopt their values for this total additional source of error , and we record them under the name of mismatch error . 
   figure [ figure1 ] shows the histograms of the @xmath54 errors for the five ultraviolet bands which we obtain using equation ( [ sigmae ] ) . 
   the errors are completely dominated by the mismatch errors which results in a few spikes observed in each panel . 
   the errors adopted by gs and marked by vertical lines are shown for comparison .    from the @xcite catalog 
   we exclude some lines of sight using the same method of selection as gs . 
   it means that we exclude the lines of sight that have @xmath67 , and the ones with @xmath68 . 
   this selection results in 923 lines of sight considered previously by gs . 
   in addition , we also exclude those stars that do not have spectral type classification , because for them we are not able to assign the mismatch errors . 
   this last cut reduces the number of lines of sight we consider to 782 . 
   figure [ figure2 ] shows the sky positions of the stars in our sample . 
   the sample contains stars of spectral type b7 and earlier and this is the reason for which almost all the stars lie in the galactic plane at low latitudes . 
   by using the method described in  [ th.cons . ] 
   we compute for our sample the @xmath0 and @xmath1 values listed in table [ table2 ] . here 
   we present only the first 20 objects from our sample . 
   the complete table is available in electronic form and on the world wide web . in the first column we list the names of stars , in the second and third 
   the galactic coordinates , then the @xmath46 values taken from @xcite catalog ; in the remaining columns we list the @xmath0 and @xmath1 values with their errors obtained using formulae ( [ rvweighted])([sigmaquadav ] ) . +    our determination of @xmath0 values with their errors is made using the gs method improved through the consistent treatment of observational errors . 
   figure [ figure3 ] shows the good agreement between the @xmath0 values obtained with gs unweighted method and the weighted method applied here . in our case , 
   the @xmath0 values are not so different between the two methods because our adopted errors in @xmath69 are of the same order in the five uv wavelengths . 
   however , it s important to notice that typical errors in @xmath0 are very different between the two methods ( mostly due to the mismatch errors considered here ) 
   . +    figure [ figure4 ] shows the same points as in figure [ figure2 ] , but now different colors mark different values of @xmath0 with @xmath0 increasing from red to blue . as expected most lines of sight 
   have @xmath0 of about 3.1 . 
   this may be also seen in figure [ figure5 ] which shows @xmath0 values as a function of galactic coordinates . 
   the circular red points are the mean values of @xmath0 for the data binned every @xmath70 and every @xmath71 for the galactic longitude and latitude , respectively . 
   these mean values do not differ a lot one from another but some sky anisotropy is also quite apparent . 
   figure [ figure6 ] shows the histogram of the @xmath0 values derived here . 
   the weighted mean of @xmath0 values is @xmath72 . 
   we consider systematic errors in extinction curve determination that can result from using biased @xmath46 values . to this aim 
   we use the wegner s ( 2002 ) calibration of @xmath46 to estimate the effect of the calibration change on the value of @xmath0 . 
   usually , the extinction curve is expressed in terms of a color excesses to @xmath46 ratio . 
   since @xmath0 value depends on this ratio ( see equation [ eps ] or [ rvweighted ] ) , the adopted @xmath46 calibration will influence it . since we do not know which set of @xmath46 values is more appropriate [ @xcite or @xcite ] , the difference in obtained @xmath0 values will be a good indicator of a possible systematic error in @xmath0 . 
   @xcite made a catalog of interstellar extinction curves of ob stars . 
   he used the uv data from @xcite , but differently from @xcite who used the data sources described in table  [ table1 ] , he took the visual magnitudes and spectral classification of o and b stars from the simbad database . 
   the maximum error in @xmath46 adopted by @xcite is 0.04 mag ; the error in @xmath73 and @xmath74 is 0.01 , and he obtains the intrinsic colors using the ` artificial ' standard method by @xcite , who found a linear relation between @xmath75 and @xmath76 and used the coefficients of this relation also to compute the linear relation between the intrinsic colors . 
   this method improves the accuracy of the intrinsic colors based on ans photometry . 
   there are 190 stars that @xcite has in common with our sample . for these stars 
   we compute the @xmath0 values using formula ( [ rvweighted ] ) weighted by the observational errors given by @xcite . 
   figure [ figure7 ] shows the difference in the @xmath0 values given by different calibrations values is : @xmath77 . ] . 
   the main effect on @xmath0 comes from the fact that the @xmath46 values from two calibrations differs on average by 0@xmath78.04 in the sense that @xcite color excesses are smaller than the ones used in our primary determination . 
   + table [ table3 ] reports results for the lines of sight in common between wegner s ( 2002 ) sample and our sample . 
   the complete table is given in the electronic form on the world wide web . 
   the first column lists stellar designations , the second and third report the galactic coordinates ; in the fourth column we list the @xmath46 values taken by @xcite ; the fifth column gives our @xmath0 values and the sixth their maximum errors ; the seventh contains the @xmath46 values used by @xcite , the eighth provides the @xmath0 values and the ninth their errors computed with our method and using the wegner s ( 2002 ) ultraviolet data . 
   using ultraviolet color excesses we find @xmath0 and @xmath1 values and their errors for a sample of 782 lines of sight . 
   we extend the analysis of @xcite by considering various sources of statistical and systematic errors . in a treatment related to the one by ducati et al . 
   ( 2003 ) , we introduce the weights associated with the errors in each uv band to our @xmath8 minimization procedure . 
   we explicitly give all the formulae we use to compute the @xmath0 and @xmath1 values and their errors . 
   we compute the maximum errors and the errors in quadrature for @xmath0 and @xmath1 taking into account mismatch errors that affect the color excesses to the largest extent . 
   we present the sky distribution of @xmath0 values and show their behavior as a function of galactic coordinates . finally , we emphasize how @xmath0 values change with different calibrations of @xmath46 . 
   since @xmath0 value may characterize entire extinction curves , extending our study into wavelength regions beyond ultraviolet will provide a check on the universality of ccm law in various parts of the spectrum . 
   we discuss this issue in the forthcoming paper . 
   we are grateful to gregory rudnick for his very careful reading of the original version of this manuscript and a number of helpful suggestions . 
   we thank paola mazzei and guido barbaro for their comments . 
   ag acknowledges the financial support from earastargal fellowship at max - planck - institute for astrophysics , where this work has been completed . 
   99 cardelli , j.  a. , clayton , g.  c. , & mathis j.  s. 1989 , , 345 , 245 cecchi - pestellini , c. , cervetto , c. , aiello , s. , & barsella , b. 1995 , , 43 , 1319 2003 , , 588 , 344 , m.  p. 1970 , a&a , 4 , 234 , e.  l. & massa , d. 1990 , , 72 , 163 , e.  l. 1999 , , 111 , 63 gnaciski , p. & sikorski , j. 1999 , , 49 , 577 , p. & greenberg , j.  m. 1993 , a&a , 274 , 439 johnson , h.l . 
   1966 , , 4 , 193 , d.  m. & savage , b.  d. 1981 , , 248 , 545 , b. 1978 , a&as , 34 , 1 , j. , krelowski , j. , & wegner , w. 1993 , , 273 , 575 , b.  d. , massa , d. , meade , m. , & wesselius , p.  r. 1985 , , 59 , 397 , w. 2002 , balta , 11 , 1 , p.  r. , van duinen , r.  j. , de jonge , a.  r.  w. , aalders , j.  w.  g. , luinge , w. , & wildeman , k.  j. 1982 , a&as , 49 , 427 , c .- c . , 
   gallagher , j.  s. , peck , m. , faber , s.  m. , & tinsley , b.  m. 1980 , , 237 , 290               values as a function of galactic coordinates . 
   the circular red points represent the unweighted mean values of @xmath0 in different coordinate bins with the rms error bars represented by the vertical lines . the galactic longitudes and latitudes are binned every @xmath79 and @xmath80 , respectively.,title="fig:",width=302 ]   values as a function of galactic coordinates . 
   the circular red points represent the unweighted mean values of @xmath0 in different coordinate bins with the rms error bars represented by the vertical lines . 
   the galactic longitudes and latitudes are binned every @xmath79 and @xmath80 , respectively.,title="fig:",width=302 ]       values obtained from equation ( [ rvweighted ] ) using the wegner s ( 2002 ) uv data versus the @xmath0 values computed with the same formula , but using our primary data . 
   the line shows a 1-to-1 relationship . 
   the right panel shows the comparison between two different calibrations of @xmath46 . 
   the line marks the average difference level.,title="fig:",width=302 ]   values obtained from equation ( [ rvweighted ] ) using the wegner s ( 2002 ) uv data versus the @xmath0 values computed with the same formula , but using our primary data . 
   the line shows a 1-to-1 relationship . 
   the right panel shows the comparison between two different calibrations of @xmath46 . 
   the line marks the average difference level.,title="fig:",width=302 ]    clcp6.5 cm @xmath74 & @xcite & 0@xmath78.04 & + @xmath76 & @xcite & 0@xmath78.015 & we adopt the same conservative errors estimate for all stars . 
   + @xmath81 & @xcite & 0@xmath78.02 & + @xmath73 & @xcite & 0@xmath78.001 - 0@xmath78.218 & + @xmath82 & @xcite & @xmath83 & + @xmath84 & @xcite & 0@xmath78.15 - 0@xmath78.40 & [ table1 ]    lccccccccc bd-84617 & 37.0 & 8.4 & 1.22 & 3.23 & 0.24 & 0.12 & 3.94 & 0.47 & 0.34 + bd-84634 & 38.0 & 7.4 & 1.22 & 2.92 & 0.25 & 0.12 & 3.56 & 0.48 & 0.34 + bd-11471 & 213.4 & 1.4 & 0.74 & 2.87 & 0.44 & 0.21 & 2.13 & 0.50 & 0.36 + bd+233762 & 60.3 & @xmath850.3 & 1.05 & 3.30 & 0.30 & 0.15 & 3.47 & 0.50 & 0.36 + bd+23771 & 37.2 & @xmath851.4 & 0.93 & 2.65 & 0.35 & 0.17 & 2.46 & 0.50 & 0.36 + bd+243893 & 61.3 & @xmath850.5 & 0.65 & 3.32 & 0.45 & 0.22 & 2.16 & 0.47 & 0.34 + bd+341054 & 173.4 & @xmath850.2 & 0.49 & 3.78 & 0.59 & 0.28 & 1.85 & 0.47 & 0.34 + bd+341059 & 173.0 & 0.2 & 0.49 & 3.75 & 0.59 & 0.29 & 1.84 & 0.47 & 0.34 + bd+341150 & 175.1 & 2.4 & 0.44 & 2.58 & 0.69 & 0.33 & 1.13 & 0.47 & 0.34 + bd+341162 & 175.5 & 2.6 & 0.36 & 2.81 & 0.84 & 0.40 & 1.01 & 0.47 & 0.34 + bd+343631 & 69.2 & 6.9 & 0.13 & 4.62 & 2.20 & 1.05 & 0.60 & 0.48 & 0.34 + bd+354258 & 77.2 & @xmath854.7 & 0.29 & 2.39 & 1.07 & 0.51 & 0.69 & 0.48 & 0.34 + bd+361261 & 174.1 & 4.3 & 0.52 & 2.75 & 0.62 & 0.30 & 1.43 & 0.50 & 0.36 + bd+363882 & 73.5 & 2.2 & 0.64 & 3.43 & 0.50 & 0.24 & 2.19 & 0.50 & 0.36 + bd+364145 & 77.5 & @xmath852.0 & 0.96 & 2.79 & 0.31 & 0.15 & 2.68 & 0.47 & 0.34 + bd+373945 & 77.3 & @xmath850.2 & 1.07 & 3.24 & 0.30 & 0.14 & 3.46 & 0.50 & 0.36 + bd+374092 & 80.2 & @xmath854.2 & 0.55 & 2.90 & 0.59 & 0.28 & 1.60 & 0.50 & 0.36 + bd+391328 & 169.1 & 3.6 & 0.88 & 2.62 & 0.37 & 0.18 & 2.30 & 0.50 & 0.36 + bd+404179 & 79.0 & 1.2 & 0.88 & 3.27 & 0.34 & 0.16 & 2.87 & 0.47 & 0.34 + bd+421286 & 166.1 & 4.3 & 0.56 & 3.12 & 0.53 & 0.26 & 1.75 & 0.47 & 0.34 [ table2 ]    l c c c c c c c c c c hd1544 & 119.3 & @xmath850.6 & 0.44 & 3.19 & 0.73 & 0.35 & 0.37 & 2.79 & 0.67 & 0.31 + hd2083 & 120.9 & 9.0 & 0.29 & 3.69 & 1.01 & 0.48 & 0.26 & 3.99 & 0.70 & 0.33 + hd2905 & 120.8 & 0.1 & 0.33 & 3.24 & 1.20 & 0.57 & 0.30 & 0.82 & 1.05 & 0.49 + hd7252 & 125.7 & @xmath851.9 & 0.35 & 3.01 & 0.86 & 0.41 & 0.32 & 3.17 & 0.68 & 0.32 + hd12867 & 133.0 & @xmath853.7 & 0.41 & 2.72 & 0.74 & 0.35 & 0.38 & 2.84 & 0.63 & 0.30 + hd13969 & 134.5 & @xmath853.8 & 0.56 & 2.73 & 0.54 & 0.26 & 0.54 & 2.88 & 0.46 & 0.21 + hd14092 & 134.7 & @xmath854.1 & 0.49 & 2.52 & 0.62 & 0.30 & 0.46 & 2.61 & 0.55 & 0.26 + hd14250 & 134.8 & @xmath853.7 & 0.58 & 2.60 & 0.52 & 0.25 & 0.55 & 2.59 & 0.49 & 0.23 + hd14357 & 135.0 & @xmath853.9 & 0.56 & 3.90 & 0.56 & 0.27 & 0.49 & 3.28 & 0.54 & 0.25 + hd14818 & 135.6 & @xmath853.9 & 0.48 & 2.63 & 0.84 & 0.40 & 0.46 & 2.17 & 0.68 & 0.31 + hd14947 & 135.0 & @xmath851.8 & 0.77 & 2.96 & 0.39 & 0.19 & 0.76 & 2.50 & 0.37 & 0.17 + hd14956 & 135.4 & @xmath852.9 & 0.89 & 2.54 & 0.45 & 0.22 & 0.88 & 2.44 & 0.36 & 0.17 + hd16429 & 135.7 & 1.1 & 0.92 & 3.12 & 0.35 & 0.17 & 0.86 & 2.72 & 0.33 & 0.16 + hd17114 & 137.3 & @xmath850.3 & 0.76 & 2.85 & 0.40 & 0.19 & 0.73 & 2.92 & 0.35 & 0.16 + hd17603 & 138.8 & @xmath852.1 & 0.92 & 2.71 & 0.44 & 0.21 & 0.92 & 2.66 & 0.31 & 0.15 + hd18352 & 137.7 & 2.1 & 0.48 & 2.91 & 0.63 & 0.30 & 0.45 & 3.07 & 0.52 & 0.24 + hd24431 & 148.8 & @xmath850.7 & 0.69 & 2.79 & 0.44 & 0.21 & 0.65 & 2.64 & 0.41 & 0.19 + hd24912 & 160.4 & @xmath8513.1 & 0.29 & 3.45 & 1.36 & 0.65 & 0.26 & 3.78 & 0.75 & 0.36 + hd30614 & 144.1 & 14.0 & 0.30 & 3.01 & 1.33 & 0.63 & 0.34 & 3.07 & 0.74 & 0.35 + hd34078 & 172.1 & @xmath852.3 & 0.52 & 3.44 & 0.57 & 0.27 & 0.49 & 3.20 & 0.49 & 0.23 [ table3 ]
summary: we present determinations of the total to selective extinction ratio @xmath0 and visual extinction @xmath1 values for milky way stars using ultraviolet color excesses . 
   we extend the analysis of @xcite by using non - equal weights derived from observational errors . 
   we present a detailed discussion of various statistical errors . 
   in addition , we estimate the level of systematic errors by considering different normalization of the extinction curve adopted by @xcite . 
   our catalog of 782 @xmath0 and @xmath1 values and their errors is available in the electronic form on the world wide web . 
   * key words : * catalogs  dust , extinction  galaxy : general  ism : structure  techniques : photometric