Jitter in interactive systems occurs when visual feedback is perceived as unstable or trembling even though the input signal is smooth or stationary. It can have multiple causes such as sensing noise, or feedback calculations introducing or exacerbating sensing imprecisions. Jitter can however occur even when each individual component of the pipeline works perfectly, as a result of the differences between the input frequency and the display refresh rate. This asynchronicity can introduce rapidly-shifting latencies between the rendered feedbacks and their display on screen, which can result in trembling cursors or viewports. % This paper contributes a better understanding of this particular type of jitter. We first detail the problem from a mathematical standpoint, from which we develop a predictive model of jitter amplitude as a function of input and output frequencies, and a new metric to measure this spatial jitter. Using touch input data gathered in a study, we developed a simulator to validate this model and to assess the effects of different techniques and settings with any output frequency. The most promising approach, when the time of the next display refresh is known, is to estimate (interpolate or extrapolate) the user's position at a fixed time interval before that refresh. % When input events occur at 125~Hz, as is common in touch screens, we show that an interval of 4 to 6~ms works well for a wide range of display refresh rates. This method effectively cancels most of the jitter introduced by input/output asynchronicity, while introducing minimal imprecision or latency.

This paper introduces the online probing" problem: In each round, the learner is able to purchase the values of a subset of feature values. After the learner uses this information to come up with a prediction for the given round, he then has the option of paying for seeing the loss that he is evaluated against. Either way, the learner pays for the imperfections of his predictions and whatever he chooses to observe, including the cost of observing the loss function for the given round and the cost of the observed features. We consider two variations of this problem, depending on whether the learner can observe the label for free or not. We provide algorithms and upper and lower bounds on the regret for both variants. We show that a positive cost for observing the label significantly increases the regret of the problem."