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Abstract Energetic particle deep penetration into low L‐shells (L < 4) impacts the dynamics of the radiation belts and ring current. Previous studies reported that electrons penetrate more frequently, deeply, and faster than protons of similar energies, but underlying mechanisms are unclear. In this study, we compare heavy‐ion behavior with electrons and protons to further identify the underlying mechanisms. Using Van Allen Probes data, we show that electron deep penetration occurs most frequently and deeply, followed by O⁺ions, then He⁺ions, and finally protons. Most particle deep penetrations occur within several hours. Superposed epoch analysis shows that prior to deep penetration, electrons have the steepest phase space density radial gradients, followed by heavy ions and then protons for the sameμandK. Our study suggests that a combination of two or more mechanisms, such as convection electric field and plasma wave‐induced scattering, may be needed to fully explain particle deep penetration. 

Battery-powered mobile devices (e.g., smartphones, AR/VR glasses, and various IoT devices) are increasingly being used for AI training due to their growing computational power and easy access to valuable, diverse, and real-time data. On-device training is highly energy-intensive, making accurate energy consumption estimation crucial for effective job scheduling and sustainable AI. However, the heterogeneity of devices and the complexity of models challenge the accuracy and generalizability of existing methods. This paper proposes AMPERE, a generic approach for energy consumption estimation in deep neural network (DNN) training. First, we examine the layer-wise energy additivity property of DNNs and strategically partition the entire model into layers for fine-grained energy consumption profiling. Then, we fit Gaussian Process (GP) models to learn from layer-wise energy consumption measurements and estimate a DNN's overall energy consumption based on its layer-wise energy additivity property. We conduct extensive experiments with various types of models across different real-world platforms. The results demonstrate that AMPERE has effectively reduced the Mean Absolute Percentage Error (MAPE) by up to 30%. Moreover, AMPERE is applied in guiding energy-aware pruning, successfully reducing energy consumption by 50%, thereby further demonstrating its generality and potential. 

We prepare quasi-1D films of Sb2Se3 on GaAs by molecular beam epitaxy. The aligned grains and anisotropic bonding hierarchy of the Sb2Se3 unit cell together produce giant birefringence in the near-infrared. 

Abstract The development of a deepening local minimum in phase space density (PSD)‐ profile indicates fast local loss potentially caused by wave‐induced scattering. The identification and characterization of proton PSD deepening minima are important for investigating the ring current loss and overall dynamics. Using multiyear Van Allen Probes observations, we analyze ∼10–100s keV proton PSD and report >100 keV proton deepening PSD minima for the first time. The overall occurrence rates of proton deepening local minimum peaks at ∼3%, mainly located at  = 4.5–5.0 near the plasmapause. The occurrence rate increases with the decrease of AL index and increase of solar wind dynamic pressure. The theoretical resonance energy of protons with typical He‐band electromagnetic ion cyclotron (EMIC) waves agrees with the energy of protons with deepening PSD minima. Thus, EMIC waves are the likely cause of the deepening PSD minimum and contribute to the fast local loss of ring current protons. 

Programmable photonic integrated circuits are expected to play an increasingly important role in enabling high-bandwidth optical interconnects and large-scale in-memory computing as needed to support the rise of artificial intelligence and machine learning technology. To that end, chalcogenide-based non-volatile phase-change materials (PCMs) present a promising solution due to zero static power. However, high switching voltage and a small number of operating levels present serious roadblocks to the widespread adoption of PCM-programmable units. Here, we demonstrate an electrically programmable wide bandgap Sb2S3-clad silicon ring resonator using a silicon microheater at a complementary-metal–oxide–semiconductor compatible voltage of <3 V. Our device shows a low switching energy of 35.33 nJ (0.48 mJ) for amorphization (crystallization) and reversible phase transitions with high endurance (>2000 switching events) near 1550 nm. Combining a volatile thermo-optic effect with non-volatile PCMs, we demonstrate 7-bit (127 levels) operation with excellent repeatability and reduced power consumption. Our demonstration of low-voltage and low-energy operation, combined with the hybrid volatile–nonvolatile approach, marks a significant step toward integrating PCM-based programmable units in large-scale optical interconnects.