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Abstract While whistler‐mode waves are generated by injected anisotropic electrons on the nightside, the observed day‐night asymmetry of wave distributions raises an intriguing question about their generation on the dayside. In this study, we evaluate the distributions of whistler‐mode wave amplitudes and electrons as a function of distance from the magnetopause (MP) on the dayside from 6 to 18 hr in magnetic local time (MLT) within ±18° of magnetic latitude using the Time History of Events and Macroscale Interaction During Substorms measurements from June 2010 to August 2018. Specifically, under different levels of solar wind dynamic pressure and geomagnetic index, we conduct a statistical analysis to examine whistler‐mode wave amplitude, as well as anisotropy and phase space density (PSD) of source electrons across 1–20 keV energies, which potentially provide a source of free energy for wave generation. In coordinates relative to the MP, we find that lower‐band (0.05–0.5f_ce) waves occur much closer to the MP than upper‐band (0.5–0.8f_ce) waves, wheref_ceis electron cyclotron frequency. Our statistical results reveal that strong waves are associated with high anisotropy and high PSD of source electrons near the equator, indicating a preferred region for local wave generation on the dayside. Over 10–14 hr in MLT, as latitude increases, electron anisotropy decreases, while whistler‐mode wave amplitudes increase, suggesting that wave propagation from the equator to higher latitudes, along with amplification along the propagation path, is necessary to explain the observed waves on the dayside. 

Transformer models have been widely investigated in different domains by providing long-range dependency handling and global contextual awareness, driving the development of popular AI applications such as ChatGPT, Gemini, and Alexa. State Space Models (SSMs) have emerged as strong contenders in the field of sequential modeling, challenging the dominance of Transformers. SSMs incorporate a selective mechanism that allows for dynamic parameter adjustment based on input data, enhancing their performance. However, this mechanism also comes with increasing computational complexity and bandwidth demands, posing challenges for deployment on resource-constraint mobile devices. To address these challenges without sacrificing the accuracy of the selective mechanism, we propose a sparse learning framework that integrates architecture-aware compiler optimizations. We introduce an end-to-end solution–C 4 n kernel sparsity, which prunes n elements from every four contiguous weights, and develop a compiler-based acceleration solution to ensure execution efficiency for this sparsity on mobile devices. Based on the kernel sparsity, our framework generates optimized sparse models targeting specific sparsity or latency requirements for various model sizes. We further leverage pruned weights to compensate for the remaining weights, enhancing downstream task performance. For practical hardware acceleration, we propose C 4 n -specific optimizations combined with a layout transformation elimination strategy. This approach mitigates inefficiencies arising from fine-grained pruning in linear layers and improves performance across other operations. Experimental results demonstrate that our method achieves superior task performance compared to other semi-structured pruning methods and achieves up-to 7→ speedup compared to llama.cpp framework on mobile devices. 

Abstract Interchange instability is known to drive fast radial transport of electrons and ions in Jupiter's inner and middle magnetosphere. In this study, we conduct a statistical survey to evaluate the properties of energetic particles and plasma waves during interchange events using Juno data from 2016 to 2023. We present representative examples of interchange events followed by a statistical analysis of the spatial distribution, duration and spatial extent. Our survey indicates that interchange instability is predominant atM‐shells from 6 to 26, peaking near 17 with an average duration of minutes and a correspondingM‐shell width of <∼0.05. During interchange events, the associated plasma waves, such as whistler‐mode, Z‐mode, and electron cyclotron harmonic waves exhibit a distinct preferential location. These findings provide valuable insights into particle transport and the source region of plasma waves in the Jovian magnetosphere, as well as in other magnetized planets within and beyond our solar system. 

Abstract We present statistical distributions of whistler‐mode chorus and hiss waves at frequencies ranging from the local proton gyrofrequency to the equatorial electron gyrofrequency (f_ce,eq) in Jupiter's magnetosphere based on Juno measurements. The chorus wave power spectral densities usually follow thef_ce,eqvariation with major wave power concentrated in the 0.05f_ce,eq–f_ce,eqfrequency range. The hiss wave frequencies are less dependent onf_ce,eqvariation than chorus with major power concentrated below 0.05f_ce,eq, showing a separation from chorus atM < 10. Our survey indicates that chorus waves are mainly observed at 5.5 < M < 13 from the magnetic equator to 20° latitude, consistent with local wave generation near the equator and damping effects. The hiss wave powers extend to 50° latitude, suggesting longer wave propagation paths without attenuation. Our survey also includes the whistler‐mode waves at high latitudes which may originate from the Io footprint, auroral hiss, or propagating hiss waves reflected to highMshells. 

Abstract Electromagnetic ion cyclotron waves in the Earth's outer radiation belt drive rapid electron losses through wave‐particle interactions. The precipitating electron flux can be high in the hundreds of keV energy range, well below the typical minimum resonance energy. One of the proposed explanations relies on nonresonant scattering, which causes pitch‐angle diffusion away from the fundamental cyclotron resonance. Here we propose the fractional sub‐cyclotron resonance, a second‐order nonlinear effect that scatters particles at resonance ordern = 1/2, as an alternate explanation. Using test‐particle simulations, we evaluate the precipitation ratios of sub‐MeV electrons for wave packets with various shapes, amplitudes, and wave normal angles. We show that the nonlinear sub‐cyclotron scattering produces larger ratios than the nonresonant scattering when the wave amplitude reaches sufficiently large values. The ELFIN CubeSats detected several events with precipitation ratio patterns matching our simulation, demonstrating the importance of sub‐cyclotron resonances during intense precipitation events. 

Abstract During geomagnetic storms relativistic outer radiation belt electron flux exhibits large variations on rapid time scales of minutes to days. Many competing acceleration and loss processes contribute to the dynamic variability of the radiation belts; however, distinguishing the relative contribution of each mechanism remains a major challenge as they often occur simultaneously and over a wide range of spatiotemporal scales. In this study, we develop a new comprehensive model for storm‐time radiation belt dynamics by incorporating electron wave‐particle interactions with parallel propagating whistler mode waves into our global test‐particle model of the outer belt. Electron trajectories are evolved through the electromagnetic fields generated from the Multiscale Atmosphere‐Geospace Environment (MAGE) global geospace model. Pitch angle scattering and energization of the test particles are derived from analytical expressions for quasi‐linear diffusion coefficients that depend directly on the magnetic field and density from the magnetosphere simulation. Using a study of the 17 March 2013 geomagnetic storm, we demonstrate that resonance with lower band chorus waves can produce rapid relativistic flux enhancements during the main phase of the storm. While electron loss from the outer radiation belt is dominated by loss through the magnetopause, wave‐particle interactions drive significant atmospheric precipitation. We also show that the storm‐time magnetic field and cold plasma density evolution produces strong, local variations of the magnitude and energy of the wave‐particle interactions and is critical to fully capturing the dynamic variability of the radiation belts caused by wave‐particle interactions. 

critical to reveal a blackbox model’s decision-making process from raw data to prediction. In this article, we use two real datasets, the MNIST handwritten digits and MIT-BIH Electrocardiogram (ECG) signals, to motivate key characteristics of discriminative features, namely adaptiveness, predictive importance and effectiveness. Then, we develop a localization framework based on adversarial attacks to effectively localize discriminative features. In contrast to existing heuristic methods, we also provide a statistically guaranteed interpretability of the localized features by measuring a generalized partial R2. We apply the proposed method to the MNIST dataset and the MIT-BIH dataset with a convolutional auto-encoder. In the first, the compact image regions localized by the proposed method are visually appealing. Similarly, in the second, the identified ECG features are biologically plausible and consistent with cardiac electrophysiological principles while locating subtle anomalies in a QRS complex that may not be discernible by the naked eye. Overall, the proposed method compares favorably with state-of-the-art competitors. Accompanying this paper is a Python library dnn-locate that implements the proposed approach. 

Abstract Energetic particle injections are commonly observed in Jupiter's magnetosphere and have important impacts on the radiation belts. We evaluate the roles of electron injections in the dynamics of whistler‐mode waves and relativistic electrons using Juno measurements and wave‐particle interaction modeling. The Juno spacecraft observed injected electron flux bursts at energies up to 300 keV atMshell ∼11 near the magnetic equator during perijove‐31. The electron injections are related to chorus wave bursts at 0.05–0.5f_cefrequencies, wheref_ceis the electron gyrofrequency. The electron pitch angle distributions are anisotropic, peaking near 90° pitch angle, and the fluxes are high during injections. We calculate the whistler‐mode wave growth rates using the observed electron distributions and linear theory. The frequency spectrum of the wave growth rate is consistent with that of the observed chorus magnetic intensity, suggesting that the observed electron injections provide free energy to generate whistler‐mode chorus waves. We further use quasilinear theory to model the impacts of chorus waves on 0.1–10 MeV electrons. Our modeling shows that the chorus waves could cause the pitch angle scattering loss of electrons at <1 MeV energies and accelerate relativistic electrons at multiple MeV energies in Jupiter's outer radiation belt. The electron injections also provide an important seed population at several hundred keV energies to support the acceleration to higher energies. Our wave‐particle interaction modeling demonstrates the energy flow from the electron injections to the relativistic electron population through the medium of whistler‐mode waves in Jupiter's outer radiation belt. 

Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized directed relations with unspecified interventions. First, we derive conditions to yield an identifiable model. Unlike classical inference, testing directed relations requires identifying the ancestors and relevant interventions of hypothesis-specific primary variables. To this end, we propose a peeling algorithm based on nodewise regressions to establish a topological order of primary variables. Moreover, we prove that the peeling algorithm yields a consistent estimator in low-order polynomial time. Second, we propose a likelihood ratio test integrated with a data perturbation scheme to account for the uncertainty of identifying ancestors and interventions. Also, we show that the distribution of a data perturbation test statistic converges to the target distribution. Numerical examples demonstrate the utility and effectiveness of the proposed methods, including an application to infer gene regulatory networks. The R implementation is available at https://github.com/chunlinli/intdag.