DelayDroid: an instrumented approach to reducing tail-time energy of Android apps

Mobile devices with 3G/4G networking often waste energy in the so-called “tail time” during which the radio is kept on even though no communication is occurring. Prior work has proposed policies to reduce this energy waste by batching network requests. However, this work is challenging to apply in practice due to a lack of mechanisms. In response, we have developed DelayDroid, a framework that allows a developer to add the needed policy to existing, unmodified Android applications (apps) with no human effort as well as no SDK/OS changes. This allows such prior work (as well as our own policies) to be readily deployed and evaluated. The DelayDroid compile-time uses static analysis and bytecode refactoring to identify method calls that send network requests and modify such calls to detour them to the DelayDroid run-time. The run-time then applies a policy to batch them, avoiding the tail time energy waste. DelayDroid also includes a cross-app communication mechanism that supports policies that optimize across multiple apps running together, and we propose a policy that does so. We evaluated the correctness and universality of the DelayDroid mechanisms on 14 popular Android apps chosen from the Google App Store. To evaluate our proposed policy, we studied three DelayDroid-enabled apps (weather forecasting, email client, and news client) running together, finding that the DelayDroid mechanisms combined with our policy can reduce 3G/4G tail time energy waste by 36%.     

Learning Detail Transfer based on Geometric Features

The visual richness of computer graphics applications is frequently limited by the difficulty of obtaining high‐quality, detailed 3D models. This paper proposes a method for realistically transferring details (specifically, displacement maps) from existing high‐quality 3D models to simple shapes that may be created with easy‐to‐learn modeling tools. Our key insight is to use metric learning to find a combination of geometric features that successfully predicts detail‐map similarities on the source mesh; we use the learned feature combination to drive the detail transfer. The latter uses a variant of multi‐resolution non‐parametric texture synthesis, augmented by a high‐frequency detail transfer step in texture space. We demonstrate that our technique can successfully transfer details among a variety of shapes including furniture and clothing.     

Towards measurable bounds on entanglement measures

While the experimental detection of entanglement provides already quite a difficult task, experimental quantification of entanglement is even more challenging, and has not yet been studied thoroughly. In this paper we discuss several issues concerning bounds on concurrence measurable collectively on copies of a given quantum state. Firstly, we concentrate on the recent bound on concurrence by (Mintert and Buchleitner in Phys Rev Lett 98:140505/1–140505/4, 2007). Relating it to the reduction criterion for separability we provide yet another proof of the bound and point out some possibilities following from the proof which could lead to improvement of the bound. Then, relating concurrence to the generalized robustness of entanglement, we provide a method allowing for construction of lower bounds on concurrence from any positive map (not only the reduction one). All these quantities can be measured as mean values of some two-copy observables. In this sense the method generalizes the Mintert–Buchleitner bound and recovers it when the reduction map is used. As a particular case we investigate the bound obtained from the transposition map. Interestingly, comparison with MB bound performed on the class of ${4\otimes 4}While the experimental detection of entanglement provides already quite a difficult task, experimental quantification of entanglement is even more challenging, and has not yet been studied thoroughly. In this paper we discuss several issues concerning bounds on concurrence measurable collectively on copies of a given quantum state. Firstly, we concentrate on the recent bound on concurrence by (Mintert and Buchleitner in Phys Rev Lett 98:140505/1–140505/4, 2007). Relating it to the reduction criterion for separability we provide yet another proof of the bound and point out some possibilities following from the proof which could lead to improvement of the bound. Then, relating concurrence to the generalized robustness of entanglement, we provide a method allowing for construction of lower bounds on concurrence from any positive map (not only the reduction one). All these quantities can be measured as mean values of some two-copy observables. In this sense the method generalizes the Mintert–Buchleitner bound and recovers it when the reduction map is used. As a particular case we investigate the bound obtained from the transposition map. Interestingly, comparison with MB bound performed on the class of 4?4{4\otimes 4} rotationally invariant states shows that the new bound is positive in regions in which the MB bound gives zero. Finally, we provide measurable upper bounds on the whole class of concurrences.