Opinion | Before meeting with Kim, Trump should look back to Reykjavik

[b]Opinion | Before meeting with Kim, Trump should look back to Reykjavik [/b] Hosting the Oscars is a difficult, thankless job. We revisited the good, bad and ugly attempts. Air conditioners are energy hogs, so if you want to go green, go off peak. Green energy is a big topic these days, and the list of ways to generate and manage energy more efficiently keeps growing. One innovative way to use energy wisely is through the use of off-peak cooling systems. Off-peak cooling is pretty ingenious because it uses energy at night when the demand is low. Air conditioning is an energy hog, and when some of the electricity needed to cool all that air is distributed over the evening and night hours, it takes some of the burden off the energy grid at the biggest demand times of the day. This redistributes the demand for electricity, helping to avoid brownouts and blackouts and also takes advantage of lower electricity rates during off-peak times. There are other benefits to off-peak cooling systems, too. Conventional air conditioners use more energy as they get hotter, and the more energy they draw, the less efficiently they use it.|Fiber Optics, the technique of transmitting light through transparent, flexible fibers of glass or plastic. The fibers, called optical fibers, can channel light over a curved path. Bundles of parallel fibers can be used to illuminate. Observe hard-to-reach places. Optical fibers of very pure glass are able to carry light over long distances ranging from a few inches or centimeters to more than 100 miles (160 km) with little dimming. Cables containing such fibers are used in certain types of communications systems. Some individual fibers are thinner than human hair. Measure less than 0.00015 inch (0.004 mm) in diameter.00015 inch (0.004 mm) in diameter. Fiber optics is based on the optical phenomenon known as total internal reflection. With the simplest form of optical fiber, light entering one end of the fiber strikes the boundary of the fiber and is reflected inward. The light travels through the fiber in a succession of zigzag reflections until it exits from the other end of the fiber. Electronic cigarettes closely resemble the real thing. You're at your favorite restaurant, enjoying a meal. A diner at the next table is puffing on a cigarette, letting out a cloud of smoke. Because smoking isn't allowed in the restaurant, you're thinking about asking the smoker to put the cigarette out. But before you protest, consider this: Your neighbor may not be smoking at all. Electronic cigarettes, also known as smokeless cigarettes, e-cigarettes, or e-cigs, are an alternative method of consuming nicotine, the addictive chemical found in tobacco. Manufacturers often design e-cigarettes to look like regular cigarettes, but they contain no tobacco and don't require a match -- or any flame at all. There's no fire, no ash and no smoky smell. E-cigarettes do not contain all of the harmful chemicals associated with smoking tobacco cigarettes, such as carbon monoxide and tar. Manufacturers and satisfied customers say the e-cigarette is a healthier alternative to tobacco cigarettes, which cause millions of deaths every year. Complicated post-processing in both spatial (e.g. things-stuff fusion) and temporal (e.g. similarity scores fusion for instance association) domains is needed to fuse predictions of different subtasks into final VPS results. However, such a decomposed pipeline suffers from several issues. First, the complicated post-processing is time-consuming and requires manual parameter tuning, which is likely to produce sub-optimal results. Second, erroneous predictions from different branches will adversely affect each other and harm the overall performance. For example, the inaccurate boxes will also lead to incomplete segmentation masks, and missing centers will deteriorate the temporal tracking results, which can barely be corrected by post-processing. Third, end-to-end training is blocked, potentially hindering the model from learning features directly optimized for the VPS task. To solve the aforementioned problems, motivated by object-centric representation learning which learns the compact and robust representations of objects, we introduce a unified end-to-end framework, Slot-VPS, as illustrated in Figure 1. All panoptic objects (including both stuff and things) in the video are represented as a unified representation named panoptic slots. 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