Left Sample Clauses

Left. Median H I Lyα absorption as a function of transverse and LOS distance from ⟨z⟩ ≈ 2.4 star-forming galaxies. The bin size is 200 pkpc and the images have been smoothed by a 2-D Gaussian with FWHM equal to the bin size. The lower limit of the color scale corresponds to the median optical depth of all pixels, while the upper limit corresponds to the highest optical depth in the map. Absorption is clearly enhanced close to galaxies, out to at least 2 pMpc in the transverse direction, but only out to ≈ 1.5 pMpc along the LOS. This anisotropy suggests large-scale infall of gas. On the other hand, on small scales the absorption declines more rapidly in the transverse direction than in the LOS direction. Right: Results after randomizing the galaxy redshifts while keeping their impact parameters fixed. The fact that the correlation does not vary systematically with distance indicates that the features in the left panel are real. single pixel can be used multiple times: once for each galaxy whose separa- tion from the pixel falls within the range plotted. This is the first published 2-D absorption map around galaxies. The map shows a strong correlation between the Lyα absorption strength and the distance to the galaxies. The right panel of Figure 3.4 shows the same data, after randomizing the galaxy redshifts. We randomize the galaxy redshifts within the Lyα
AutoNDA by SimpleDocs
Left vertical events; fitted position of the maximum of the charge distribution in VEM as a function of the impact distance to the WCD center. Right: inclined events; fitted position of the maximum of the charge distribution in VEM as a function of the track-length. In both figures the charge is obtained from the sum of the three PMTs. uncertainties of the fit. One can see that there is no dependence on the distance, as observed in the simulations (orange squares): the bottom plot shows that the ratio between the measurements and simulations is within 1%, i.e. at the level of the signal uncertainty, thus giving a further verification of the accuracy of the simulation. P09002 To extend the study to 4 > 1.2 m, we exploit the data collected by the hodoscope in the inclined configuration. The result is shown in the right panel of figure 5, where we compare the fitted position of the maxima of the charge distributions3 determined with measurements (black dots) and simulations (orange squares), as a function of 4. The expected increase of the charge as 4 increases is consistently observed in data and simulations: as shown in the bottom inset, also for inclined events the agreement is at the level of 1%.
Left. Clustering coefficients of the richest and poorest deciles are far above the expectation for random ensembles. While the clustering emerges faster for the poorest than for the wealthiest decile, both seem to converge to similar levels. The confidence interval for the null hypothesis that the geographical distribution is purely random is calculated via eq. (6) and sampling random matrices. Right: Autocorrelation function (ACF) for the wealthiest and poorest deciles in x and y direction. All show essentially the same behaviour and a notably higher value at the first nontrivial distance than for the random matrix. The parameters used for this simulation are Tu = 200, N = 64, c = 0.6 and r = 1.4 for both graphs. neighbours from within their own group. Using the clus- tering coefficient defined in section III, we find that in the given parameter setting, both quantiles converge to the same level of clustering and far exceed the expectation for random matrices, indicating that a significant level of clustering emerges from the system’s dynamics (fig. 3 left panel). We further find that (after normalising the autocorrela- tion matrix with its maximum) there are no noteworthy differences between the row-wise and column-wise au- tocorrelation (corresponding to the x and y directions). Both directions for both the top and bottom decile show a 0.9 Relative Cost c 0.3 0.1 0.0 0.0 Top Clustering 1.0 0.8 0.7 0.6 0.5 0.3 0.2 Bottom Clustering 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.25 0.50 0.75 1.0 1.25 1.5 1.75 2.0 0.0 0.25 0.50 0.75 1.0 1.25 1.5 1.75 2.0 Relative Reward r Relative Reward r FIG. 4. For Tu = 200 and N = 64, c and r are varied and the clustering coefficients for the top and bottom decile are recorded after the last iteration. Note that c = r = 0.0 means the absence of gambling dynamics and, therefore, shows no dynamical behaviour at all. The regime for high clustering of the bottom decile has contours given by the black lines. These lines correspond to the onset of degenerate regimes for Fmin / max and are derived analytically as equations (9) and (10) in section V A. notably higher autocorrelation for the first nontrivial lag than the random matrix, thereby confirming the analysis of the clustering coefficients that rich and poor neigh- bourhoods emerge in the system (fig. 3 right panel).
Left. Example 1 scenario, where 100% of the 2 acre area was walked and treated.
Left. Sketch of a more realistic geometry of a friction measurement with an FFM. The tip is shown as a small piece of flat surface that consists of a few atoms that are touching the substrate simultaneously. Right: One-dimensional potential picture of the two contacting surfaces. Here, a1 is the surface lattice constant and a2 is the lattice constant of the tip. U1 is the potential corrugation of the surface experienced by each of the tip atoms. multi-atom area of contact. The reason is that the combined potential of all interactions over the contact area still exhibits a period, e.g. sinusoidal, variation with the periodicity of the substrate lattice [16].
Left. Noise recorded by the sensor when the tip is not in contact with the surface. The signal is the combination of thermal, laser and electronic noise in the detection part of the instrument. The sweep time was 100 ms and the band width of the low-pass filter was set to 1 kHz. Right: Averaged Fast Fourier Transform (FFT) of signals showing no special frequency appearing in the spectrum. Let us consider the thermal vibrations of the cantilever. The amplitude (root mean square displacement) XT, with which the cantilever is vibrating is determined by the thermal energy stored in the cantilever [6].
Left. (A) and (B) indicate regions on the (graphite) lattice where the surface corrugations are rather different. The scan lines in region (B) are expected to exhibit lower friction than those in (A). Right: measured friction force as a function of the Y- coordinate of the scan line within a lateral-force image on graphite. At regular intervals, corresponding to the two distances between carbon atom rows along the Y- direction of 0.15 and 0.25 nm, the friction force shows a sharp minimum. The straight grey line indicates the average friction force of the entire lateral-force image. The two-dimensional structure of the surface leads to distinct variations in the potential energy corrugation as a function of the precise location of the scan line on the surface. These variations are particularly strong when the scan direction is aligned with one of the crystallographic directions of the substrate. Such effects have been reported recently by Xxxxxxxxxxx and colleagues on a graphite lattice [12]. This variation and the corresponding periodic variation in the observed stick- slip period was already discussed briefly in Chapter 3, where we used only a selection of force loops, corresponding to single-period, one-dimensional type trajectories over the two-dimensional surface. The left panel of Fig. 4.10 indicates two bands of scan lines where we may expect the surface corrugations to be rather different.
AutoNDA by SimpleDocs
Left. The two-dimensional model potential combining the interaction of the tip with the 2D graphite lattice and the elastic energy of a cantilever with an effective spring constant along both X- and Y-directions of 0.5 N/m. The intersections of the minima in X and Y directions shows the possible stable potential minima, to which the tip can jump. Different lines weaved parallel to the x-axis show the geometrical variations in surface corrugations that cause the geometrical lubricity effect discussed in section 4.6 b.
Left. The installation of the MOMA system on a vehicle. Such an effort requires only around 15 minutes and does not need skilled operators. Right. The real- time user navigation interface used to operate the MOMA system. Figure 7 Example of an unprojected omnidirectional image. The horizontal field of view is 360°, the vertical field of view is around 170°. The vehicle is usually operated by two persons: a driver and a navigator. The navigator has the duty of assisting the driver in following the planned optimized route. In case the optimized route cannot be followed (for instance, because of unexpected traffic jams, works in progress, accidents, etc.) the navigator can choose a different route using his or her own judgement. The total adherence to the planned route is not fundamental. What is important is to comply as much as possible with the sampling distribution that originated from the particular route. A simple real-time navigation interface can be easily realized with a laptop running the QGIS platform, and a connected portable GPS (Figure 6). As this is a full-featured GIS platform, it is possible to show on the underlying map not only the actual path of the MOMA and the planned one, but also any other ancillary geo-information which can help the navigator to carry out their duty. The MOMA system is battery-operated and can be driven for up to 6 hours before requiring re- charging (with an optional second battery, naturally the operation time can be increased). In a normal day (6 hours) of operation in a dense urban environment, the system can drive more than 100 km and acquire tens of thousands of geo-referenced images. In the post-acquisition phase, the captured images are stitched and saved as equirectangular xxxxxxxxx xxxxxx, and a set of metadata is embedded in order to keep a record of the acquisition parameters, i.e., the geographical coordinates, the processing phases and any other information which can be relevant in the subsequent phases. Once the survey is terminated, the images can be transferred to the REM database, described in the deliverable DB2, and analysed through the RRVS interface described in the following. Analysis of data collected through the REM RRVS web platform‌ The Rapid Remote Visual Screening is a modern version of the well-known Rapid Visual Screening methodology (see ATC-13 and FEMA-1544 methodologies), largely used in the engineering community. The geographic locations where the images have been captured are stored in a ...
Left. 1 The arbitrator's decision shall be final and binding upon the Employer and the Association; 2 provided, however, that the arbitrator shall not, without specific written agreement of the 3 Employer and the Association with respect to the arbitration proceeding before him/her, be 4 authorized to add to, detract from or in any way alter the provisions of this Agreement. 6 The arbitrator's pay and all jointly incurred incidental expenses of the arbitration shall be 7 borne equally by the parties. If a court-reported transcript is requested by a party and 8 used by both parties that cost shall also borne by both parties. If only one of the parties 9 utilizes the transcript, the full cost of the court-reported transcript shall be borne by the 10 party requesting the court reporter. However, each party shall bear the other expenses 11 of presenting its own case.
Time is Money Join Law Insider Premium to draft better contracts faster.