PUBLIC MARKS with search non-performance

Fortunately, just few days before, Opera folks came out with an experimental “style profiler” (followed by WebKit’s ticket+patch shortly after). The profiler was meant to reveal the performance of CSS selector matching, document reflow, repaint, and even document and css parsing times.

A user’s cache can only hold less than a day’s worth of browsing data: An average user browses 88 pages/day, an average page weighs 930KB, and most desktop browsers cache no more than 75MB of data. For mobile, the ratio is even worse.

As a real world example of how big the impact can be on performance, I made a test with the full HTML5 specification downloaded locally and ran the profiler. I noticed few of the most expensive selectors had ":link" and ":visited". I changed these to instead be "a:link" or "a:visited" (in this case they were equivalent in terms of which elements matched). This trivial change makes the engine use a cache, and bought the selector matching time down from about 14 seconds to about 11 seconds.

Since Apotheker didn't stick around long enough to collect a performance bonus , the company will just use his bonus target as the "average," which is equal to twice his salary.

En même temps, le "performance bonus" c'est normalement un bonus donné en fonction de la performance, pas sûr qu'il en eu mérité un, même en restant plus longtemps.

Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. The emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally — on the scale of the entire planet! In this paper, we propose a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach. For this task, we will leverage a dataset of over 6 million GPS-tagged images from the Internet. We represent the estimated image location as a probability distribution over the Earth's surface. We quantitatively evaluate our approach in several geolocation tasks and demonstrate encouraging performance (up to 30 times better than chance). We show that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.