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CNNs (Convolutional Neural Networks) are becoming increasingly important for real-time applications, such as image classification in traffic control, visual surveillance, and smart manufacturing. It is challenging, however, to meet timing constraints of image processing tasks using CNNs due to their complexity. Performing dynamic trade-offs between the inference accuracy and time for image data analysis in CNNs is challenging too, since we observe that more complex CNNs that take longer to run even lead to lower accuracy in many cases by evaluating hundreds of CNN models in terms of time and accuracy using two popular data sets, MNIST and CIFAR-10. To address these challenges, we propose a new approach that (1) generates CNN models and analyzes their average inference time and accuracy for image classification, (2) stores a small subset of the CNNs with monotonic time and accuracy relationships offline, and (3) efficiently selects an effective CNN expected to support the highest possible accuracy among the stored CNNs subject to the remaining time to the deadline at run time. In our extensive evaluation, we verify that the CNNs derived by our approach are more flexible and cost-efficient than two baseline approaches. We verify that our approach can effectively build a compact set of CNNs and efficiently support systematic time vs. accuracy trade-offs, if necessary, to meet the user-specified timing and accuracy requirements. Moreover, the overhead of our approach is little/acceptable in terms of latency and memory consumption. 

Emerging virtual and augmented reality applications are envisioned to significantly enhance user experiences. An important issue related to user experience is thermal management in smartphones widely adopted for virtual and augmented reality applications. Although smartphone overheating has been reported many times, a systematic measurement and analysis of their thermal behaviors is relatively scarce, especially for virtual and augmented reality applications. To address the issue, we build a temperature measurement and analysis framework for virtual and augmented reality applications using a robot, infrared cameras, and smartphones. Using the framework, we analyze a comprehensive set of data including the battery power consumption, smartphone surface temperature, and temperature of key hardware components, such as the battery, CPU, GPU, and WiFi module. When a 360◦ virtual reality video is streamed to a smartphone, the phone surface temperature reaches near 39◦C. Also, the temperature of the phone surface and its main hardware components generally increases till the end of our 20-minute experiments despite thermal control undertaken by smartphones, such as CPU/GPU frequency scaling. Our thermal analysis results of a popular AR game are even more serious: the battery power consumption frequently exceeds the thermal design power by 20–80%, while the peak battery, CPU, GPU, and WiFi module temperature exceeds 45, 70, 70, and 65◦C, respectively 

Measuring the behavior of redox-active molecules in space and time is crucial for understanding chemical and biological systems and for developing new technologies. Optical schemes are noninvasive and scalable, but usually have a slow response compared to electrical detection methods. Furthermore, many fluorescent molecules for redox detection degrade in brightness over long exposure times. Here, we show that the photoluminescence of “pixel” arrays of monolayer MoS 2 can image spatial and temporal changes in redox molecule concentration. Because of the strong dependence of MoS 2 photoluminescence on doping, changes in the local chemical potential substantially modulate the photoluminescence of MoS 2 , with a sensitivity of 0.9 mV / Hz on a 5 μm × 5 μm pixel, corresponding to better than parts-per-hundred changes in redox molecule concentration down to nanomolar concentrations at 100-ms frame rates. This provides a new strategy for visualizing chemical reactions and biomolecules with a two-dimensional material screen. 

We report on a search for a resonance $X$decaying to a pair of muons in $e^{+}{e}^{-}\to {\mu }^{+}{\mu }^{-}X$events in the $0.212–9.000\mathrm{GeV}/c^2$mass range, using $178{\mathrm{fb}}^{-1}$of data collected by the Belle II experiment at the SuperKEKB collider at a center of mass energy of 10.58 GeV. The analysis probes two different models of $X$beyond the standard model: a $Z^{\prime }$vector boson in the $L_{\mu }-L_{\tau }$model and a muonphilic scalar. We observe no evidence for a signal and set exclusion limits at the 90% confidence level on the products of cross section and branching fraction for these processes, ranging from 0.046 fb to 0.97 fb for the $L_{\mu }-L_{\tau }$model and from 0.055 fb to 1.3 fb for the muonphilic scalar model. For masses below $6\mathrm{GeV}/c^2$, the corresponding constraints on the couplings of these processes to the standard model range from 0.0008 to 0.039 for the $L_{\mu }-L_{\tau }$model and from 0.0018 to 0.040 for the muonphilic scalar model. These are the first constraints on the muonphilic scalar from a dedicated search. Published by the American Physical Society2024