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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. 

Interchange instability is known to drive fast radial transport of particles in Jupiter's inner magnetosphere. Magnetic flux tubes associated with the interchange instability often coincide with changes in particle distributions and plasma waves, but further investigations are required to understand their detailed characteristics. We analyze representative interchange events observed by Juno, which exhibit intriguing features of particle distributions and plasma waves, including Z‐mode and whistler‐mode waves. These events occurred at an equatorial radial distance of ∼9 Jovian radii on the nightside, with Z‐mode waves observed at mid‐latitude and whistler‐mode waves near the equator. We calculate the linear growth rate of whistler‐mode and Z‐mode waves based on the observed plasma parameters and electron distributions and find that both waves can be locally generated within the interchanged flux tube. Our findings are important for understanding particle transport and generation of plasma waves in the magnetospheres of Jupiter and other planetary systems. 

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. 

The subphylum Saccharomycotina is a lineage in the fungal phylum Ascomycota that exhibits levels of genomic diversity similar to those of plants and animals. The Saccharomycotina consist of more than 1 200 known species currently divided into 16 families, one order, and one class. Species in this subphylum are ecologically and metabolically diverse and include important opportunistic human pathogens, as well as species important in biotechnological applications. Many traits of biotechnological interest are found in closely related species and often restricted to single phylogenetic clades. However, the biotechnological potential of most yeast species remains unexplored. Although the subphylum Saccharomycotina has much higher rates of genome sequence evolution than its sister subphylum, Pezizomycotina , it contains only one class compared to the 16 classes in Pezizomycotina . The third subphylum of Ascomycota , the Taphrinomycotina , consists of six classes and has approximately 10 times fewer species than the Saccharomycotina . These data indicate that the current classification of all these yeasts into a single class and a single order is an underappreciation of their diversity. Our previous genome-scale phylogenetic analyses showed that the Saccharomycotina contains 12 major and robustly supported phylogenetic clades; seven of these are current families ( Lipomycetaceae , Trigonopsidaceae , Alloascoideaceae , Pichiaceae , Phaffomycetaceae , Saccharomycodaceae , and Saccharomycetaceae ), one comprises two current families ( Dipodascaceae and Trichomonascaceae ), one represents the genus Sporopachydermia , and three represent lineages that differ in their translation of the CUG codon (CUG-Ala, CUG-Ser1, and CUG-Ser2). Using these analyses in combination with relative evolutionary divergence and genome content analyses, we propose an updated classification for the Saccharomycotina , including seven classes and 12 orders that can be diagnosed by genome content. This updated classification is consistent with the high levels of genomic diversity within this subphylum and is necessary to make the higher rank classification of the Saccharomycotina more comparable to that of other fungi, as well as to communicate efficiently on lineages that are not yet formally named. 

Electromagnetic ion cyclotron (EMIC) waves can drive radiation belt depletion and Low‐Earth Orbit satellites can detect the resulting electron and proton precipitation. The ELFIN (Electron Losses and Fields InvestigatioN) CubeSats provide an excellent opportunity to study the properties of EMIC‐driven electron precipitation with much higher energy and pitch‐angle resolution than previously allowed. We collect EMIC‐driven electron precipitation events from ELFIN observations and use POES (Polar Orbiting Environmental Satellites) to search for 10s–100s keV proton precipitation nearby as a proxy of EMIC wave activity. Electron precipitation mainly occurs on localized radial scales (∼0.3 L), over 15–24 MLT and 5–8 L shells, stronger at ∼MeV energies and weaker down to ∼100–200 keV. Additionally, the observed loss cone pitch‐angle distribution agrees with quasilinear predictions at ≳250 keV (more filled loss cone with increasing energy), while additional mechanisms are needed to explain the observed low‐energy precipitation.

 

In this study, using Van Allen Probes observations we identify 81 events of electron flux bursts with butterfly pitch angle distributions for tens of keV electrons with close correlations with chorus wave bursts in the Earth's magnetosphere. We use the high‐rate electron flux data from Magnetic Electron Ion Spectrometer available during 2013–2019 and the simultaneous whistler‐mode wave measurements from Electric and Magnetic Field Instrument Suite and Integrated Science to identify the correlated events. The events are categorized into 67 upper‐band chorus (0.5–0.8f_ce) dominated events and 14 other events where lower‐band chorus (0.05–0.5f_ce) has modest or strong amplitudes (f_cerepresents electron cyclotron frequency). Each electron flux burst correlated with chorus has a short timescale of ∼1 min or less, suggesting potential nonlinear effects. The statistical distribution of selected electron burst events tends to occur in the post‐midnight sector atL > 5 under disturbed geomagnetic conditions, and is associated with chorus waves with relatively strong magnetic wave amplitude and small wave normal angle. The frequency dependence of the electron flux peaks agrees with the cyclotron resonant condition, indicating the effects of chorus‐induced electron acceleration. Our study provides new insights into understanding the rapid nonlinear interactions between chorus and energetic electrons.

 

Electron diffraction through a thin patterned silicon membrane can be used to create complex spatial modulations in electron distributions. By precisely varying parameters such as crystallographic orientation and wafer thickness, the intensity of reflections in the diffraction plane can be controlled and by placing an aperture to block all but one spot, we can form an image with different parts of the patterned membrane, as is done for bright-field imaging in microscopy. The patterned electron beams can then be used to control phase and amplitude of subsequent x-ray emission, enabling novel coherent x-ray methods. The electrons themselves can also be used for femtosecond time resolved diffraction and microscopy. As a first step toward patterned beams, we demonstrate experimentally and through simulation the ability to accurately predict and control diffraction spot intensities. We simulate MeV transmission electron diffraction patterns using the multislice method for various crystallographic orientations of a single crystal Si(001) membrane near beam normal. The resulting intensity maps of the Bragg reflections are compared to experimental results obtained at the Accelerator Structure Test Area Ultrafast Electron Diffraction (ASTA UED) facility at SLAC. Furthermore, the fraction of inelastic and elastic scattering of the initial charge is estimated along with the absorption of the membrane to determine the contrast that would be seen in a patterned version of the Si(001) membrane. 

In spite of its urgent importance in the era of big data, testing high-dimensional parameters in generalized linear models (GLMs) in the presence of high-dimensional nuisance parameters has been largely under-studied, especially with regard to constructing powerful tests for general (and unknown) alternatives. Most existing tests are powerful only against certain alternatives and may yield incorrect Type I error rates under high-dimensional nuisance parameter situations. In this paper, we propose the adaptive interaction sum of powered score (aiSPU) test in the framework of penalized regression with a non-convex penalty, called truncated Lasso penalty (TLP), which can maintain correct Type I error rates while yielding high statistical power across a wide range of alternatives. To calculate its p-values analytically, we derive its asymptotic null distribution. Via simulations, its superior finite-sample performance is demonstrated over several representative existing methods. In addition, we apply it and other representative tests to an Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set, detecting possible gene-gender interactions for Alzheimer’s disease. We also put R package “aispu” implementing the proposed test on GitHub. 

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data collected by the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ∼17 s, or$\Delta L\sim 0.56$$\Delta L\sim 0.56$) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same MLT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using multiple years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ∼50 events of strong EMIC wave-driven precipitation. Most reside at$L\sim 5-7$$L\sim 5-7$at dusk, while a smaller subset exists at$L\sim 8-12$$L\sim 8-12$at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an$L$$L$-shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio’s spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of$\sim 1.45$$\sim 1.45$MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive treatment likely includes effects from nonlinear resonant interactions (especially at high energies) and nonresonant effects from sharp wave packet edges (at low energies). Sub-MeV electron precipitation observed concurrently with strong EMIC wave-driven >1 MeV precipitation has a spectral shape that is consistent with efficient pitch-angle scattering down to ∼ 200-300 keV by much less intense higher frequency EMIC waves at dusk (where such waves are most frequent). At ∼100 keV, whistler-mode chorus may be implicated in concurrent precipitation. These results confirm the critical role of EMIC waves in driving relativistic electron losses. Nonlinear effects may abound and require further investigation.