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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. 

A<sc>bstract</sc> We perform the first search forCPviolation in$$ {D}_{(s)}^{+}\to {K}_S^0{K}^{-}{\pi}^{+}{\pi}^{+} $$ $D_{\left(s\right)}^{+}\to K_S^0{K}^{-}{\pi }^{+}{\pi }^{+}$decays. We use a combined data set from the Belle and Belle II experiments, which studye⁺e⁻collisions at center-of-mass energies at or near the Υ(4S) resonance. We use 980 fb⁻¹of data from Belle and 428 fb⁻¹of data from Belle II. We measure sixCP-violating asymmetries that are based on triple products and quadruple products of the momenta of final-state particles, and also the particles’ helicity angles. We obtain a precision at the level of 0.5% for$$ {D}^{+}\to {K}_S^0{K}^{-}{\pi}^{+}{\pi}^{+} $$ $D^{+}\to K_S^0{K}^{-}{\pi }^{+}{\pi }^{+}$decays, and better than 0.3% for$$ {D}_s^{+}\to {K}_S^0{K}^{-}{\pi}^{+}{\pi}^{+} $$ $D_s^{+}\to K_S^0{K}^{-}{\pi }^{+}{\pi }^{+}$decays. No evidence ofCPviolation is found. Our results for the triple-product asymmetries are the most precise to date for singly-Cabibbo-suppressedD⁺decays. Our results for the other asymmetries are the first such measurements performed for charm decays. 

We measure the branching fraction and $CP$-violating flavor-dependent rate asymmetry of $B^0\to {\pi }^0{\pi }^0$decays reconstructed using the Belle II detector in an electron-positron collision sample containing $387\times {10}^6\mathrm{\Upsilon }\left(4S\right)$mesons. Using an optimized event selection, we find $125\pm 20$signal decays in a fit to background-discriminating and flavor-sensitive distributions. The resulting branching fraction is $(1.25\pm 0.23)\times {10}^{-6}$and the $CP$-violating asymmetry is $0.03\pm 0.30$. Published by the American Physical Society2025 

A<sc>bstract</sc> We report measurements of the absolute branching fractions$$\mathcal{B}\left({B}_{s}^{0}\to {D}_{s}^{\pm }X\right)$$,$$\mathcal{B}\left({B}_{s}^{0}\to {D}^{0}/{\overline{D} }^{0}X\right)$$, and$$\mathcal{B}\left({B}_{s}^{0}\to {D}^{\pm }X\right)$$, where the latter is measured for the first time. The results are based on a 121.4 fb⁻¹data sample collected at the Υ(10860) resonance by the Belle detector at the KEKB asymmetric-energye⁺e⁻collider. We reconstruct one$${B}_{s}^{0}$$meson in$${e}^{+}{e}^{-}\to \Upsilon\left(10860\right)\to {B}_{s}^{*}{\overline{B} }_{s}^{*}$$events and measure yields of$${D}_{s}^{+}$$,D⁰, andD⁺mesons in the rest of the event. We obtain$$\mathcal{B}\left({B}_{s}^{0}\to {D}_{s}^{\pm }X\right)=\left(68.6\pm 7.2\pm 4.0\right)\%$$,$$\mathcal{B}\left({B}_{s}^{0}\to {D}^{0}/{\overline{D} }^{0}X\right)=\left(21.5\pm 6.1\pm 1.8\right)\%$$, and$$\mathcal{B}\left({B}_{s}^{0}\to {D}^{\pm }X\right)=\left(12.6\pm 4.6\pm 1.3\right)\%$$, where the first uncertainty is statistical and the second is systematic. Averaging with previous Belle measurements gives$$\mathcal{B}\left({B}_{s}^{0}\to {D}_{s}^{\pm }X\right)=\left(63.4\pm 4.5\pm 2.2\right)\%$$and$$\mathcal{B}\left({B}_{s}^{0}\to {D}^{0}/{\overline{D} }^{0}X\right)=\left(23.9\pm 4.1\pm 1.8\right)\%$$. For the$${B}_{s}^{0}$$production fraction at the Υ(10860), we find$${f}_{s}=\left({21.4}_{-1.7}^{+1.5}\right)\%$$.