Slowly cooling white dwarfs in M13 from stable hydrogen burning

White Dwarfs (WDs) are the final evolutionary product of the vast majority of stars in the Universe. They are electron-degenerate structures characterized by no stable thermonuclear activity, and their evolution is generally described as a pure cooling process. Their cooling rate is adopted as cosmic chronometer to constrain the age of several Galactic populations, including the disk, globular and open clusters. By analysing high-resolution photometric data of two twin Galactic globular clusters (M3 and M13), we find a clear-cut and unexpected over-abundance of bright WDs in M13. Theoretical models suggest that, consistently with the horizontal branch morphology, this over-abundance is due to a slowing down of the cooling process in ~70% of the WDs in M13, caused by stable thermonuclear burning in their residual hydrogen-rich envelope. This is the first observational evidence of quiescent thermonuclear activity occurring in cooling WDs and it brings new attention on the use of the WD cooling rate as cosmic chronometer for low metallicity environments.

Bursty magnetic reconnection at the Earths magnetopause triggered by high-speed jets

The impact of high-speed jets -- dynamic pressure enhancements in the magnetosheath -- on the Earths magnetopause has been observed to trigger local magnetic reconnection. We perform a three-dimensional hybrid simulation to study the magnetosheath and magnetopause under turbulent conditions using a quasi-radial southward interplanetary magnetic field (IMF). In contrast to quasi-steady reconnection with a strong southward IMF, we show that after the impact of a jet on the magnetopause, the magnetopause moves inwards, the current sheet is compressed and intensified and signatures of local magnetic reconnection are observed, showing similarities to spacecraft measurements

Nonlinear Autoregression with Convergent Dynamics on Novel Computational Platforms

Nonlinear stochastic modeling is useful for describing complex engineering systems. Meanwhile, neuromorphic (brain-inspired) computing paradigms are developing to tackle tasks that are challenging and resource intensive on digital computers. An emerging scheme is reservoir computing which exploits nonlinear dynamical systems for temporal information processing. This paper introduces reservoir computers with output feedback as stationary and ergodic infinite-order nonlinear autoregressive models. We highlight the versatility of this approach by employing classical and quantum reservoir computers to model synthetic and real data sets, further exploring their potential for control applications.

Delocalized states in three-terminal superconductor-semiconductor nanowire devices

We fabricate three-terminal hybrid devices with a nanowire segment proximitized by a superconductor, and with two tunnel probe contacts on either side of that segment. We perform simultaneous tunneling measurements on both sides. We identify some states as delocalized above-gap states observed on both ends, and some states as localized near one of the tunnel barriers. Delocalized states can be traced from zero to finite magnetic fields beyond 0.5 T. In the parameter regime of delocalized states, we search for correlated subgap resonances required by the Majorana zero mode hypothesis. While both sides exhibit ubiquitous low-energy features at high fields, no correlation is inferred. Simulations using a one-dimensional effective model suggest that delocalized states may belong to lower one-dimensional subbands, while the localized states originate from higher subbands. To avoid localization in higher subbands, disorder may need to be further reduced to realize Majorana zero modes.

Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning

The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. However, most prognostic models are either based on histology or genomics alone and do not address how histology and genomics can be integrated to develop joint image-omic prognostic models. Additionally identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We used multimodal deep learning to integrate gigapixel whole slide pathology images, RNA-seq abundance, copy number variation, and mutation data from 5,720 patients across 14 major cancer types. Our interpretable, weakly-supervised, multimodal deep learning algorithm is able to fuse these heterogeneous modalities for predicting outcomes and discover prognostic features from these modalities that corroborate with poor and favorable outcomes via multimodal interpretability. We compared our model with unimodal deep learning models trained on histology slides and molecular profiles alone, and demonstrate performance increase in risk stratification on 9 out of 14 cancers. In addition, we analyze morphologic and molecular markers responsible for prognostic predictions across all cancer types. All analyzed data, including morphological and molecular correlates of patient prognosis across the 14 cancer types at a disease and patient level are presented in an interactive open-access database (http://pancancer.mahmoodlab.org) to allow for further exploration and prognostic biomarker discovery. To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied.

Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks

Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival. Despite the advancements made in weakly-supervised deep learning, many approaches are not context-aware and are unable to model important morphological feature interactions between cell identities and tissue types that are prognostic for patient survival. In this work, we present Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional network that hierarchically aggregates instance-level histology features to model local- and global-level topological structures in the tumor microenvironment. We validate Patch-GCN with 4,370 gigapixel WSIs across five different cancer types from the Cancer Genome Atlas (TCGA), and demonstrate that Patch-GCN outperforms all prior weakly-supervised approaches by 3.58-9.46%. Our code and corresponding models are publicly available at https://github.com/mahmoodlab/Patch-GCN.

Role of intermetallic phases in initiation and propagation of intergranular corrosion of an Al-Li-Cu-Mg alloy

Intermetallic phases in a recently developed Al-Li-Cu-Mg alloy have been investigated to understand their roles in the initiation and propagation processes of intergranular corrosion. Corrosion initiation involves trenching formation in the Al matrix adjacent to the large particles of Al7Cu2(Fe, Mn) phases. These phases containing Li are electrochemically active and susceptible to self-dissolution via a de-alloying mechanism during corrosion process. The subsurface particles of Al7Cu2(Fe, Mn) and Al20Cu2Mn3 phases act as the internal cathodes for continuous corrosion propagation along the particle-matrix interface and the associated grain boundaries. Corrosion propagation along the particle-matrix interface was facilitated by the anodic dissolution of the surrounding Al matrix due to the micro-galvanic interaction with the cathodic intermetallic phases. In addition, T1 (Al2CuLi) precipitates and the isolated particles of Al7Cu2(Fe, Mn) and Al20Cu2Mn3 phases were dissolved along the path of corrosion propagation. The dissolved metal ions were redeposited through the network of crevice.

Chandra Observations of Excess Fe K$alpha$ Line Emission in Galaxies with High Star Formation Rates: X-ray Reflection on Galaxy Scales?

In active galactic nuclei (AGN), fluorescent Fe K$alpha$ (iron) line emission is generally interpreted as originating from obscuring material around a supermassive black hole (SMBH) on the scale of a few parsecs (pc). However, recent Chandra studies indicate the existence of iron line emission extending to kpc scales in the host galaxy. The connection between iron line emission and large-scale material can be spatially resolved directly only in nearby galaxies, but could be inferred in more distant AGNs by a connection between line emission and star-forming gas and dust that is more extended than the pc-scale torus. Here we present the results from a stacking analysis and X-ray spectral fitting performed on sources in the Chandra Deep Field South (CDFS) 7 Ms observations. From the deep stacked spectra, we select sources with stellar mass $log(M_*/M_odot)>10$ at $0.5<z<2$, obtaining 25 sources with high infrared luminosity ($ {rm SFR}_{rm FIR} geq 17;M_{odot};{rm yr}^{-1}$) and 32 sources below this threshold. We find that the equivalent width of the iron line EW(Fe) is a factor of three higher with 3$sigma$ significance for high infrared luminosity measured from Herschel observations, indicating a connection between iron line emission and star-forming material on galaxy scales. We show that there is no significant dependence in EW(Fe) on $M_*$ or X-ray luminosity, suggesting the reflection of AGN X-ray emission over large scales in their host galaxies may be widespread.

Characterization and suppression of background light shifts in an optical lattice clock

Experiments involving optical traps often require careful control of the ac Stark shifts induced by strong confining light fields. By carefully balancing light shifts between two atomic states of interest, optical traps at the magic wavelength have been especially effective at suppressing deleterious effects stemming from such shifts. Highlighting the power of this technique, optical clocks today exploit Lamb-Dicke confinement in magic-wavelength optical traps, in some cases realizing shift cancellation at the ten parts per billion level. Theory and empirical measurements can be used at varying levels of precision to determine the magic wavelength where shift cancellation occurs. However, lasers exhibit background spectra from amplified spontaneous emission or other lasing modes which can easily contaminate measurement of the magic wavelength and its reproducibility in other experiments or conditions. Indeed, residual light shifts from laser background have plagued optical lattice clock measurements for years. In this work, we develop a simple theoretical model allowing prediction of light shifts from measured background spectra. We demonstrate good agreement between this model and measurements of the background light shift from an amplified diode laser in an Yb optical lattice clock. Additionally, we model and experimentally characterize the filtering effect of a volume Bragg grating bandpass filter, demonstrating that application of the filter can reduce background light shifts from amplified spontaneous emission well below the $10^{-18}$ fractional clock frequency level. This demonstration is corroborated by direct clock comparisons between a filtered amplified diode laser and a filtered titanium:sapphire laser.

Quantum-enhanced radiometry via approximate quantum error correction

By exploiting the exotic quantum states of a probe, it is possible to realize efficient sensors that are attractive for practical metrology applications and fundamental studies. Similar to other quantum technologies, quantum sensing is suffering from noises and thus the experimental developments are hindered. Although theoretical schemes based on quantum error correction (QEC) have been proposed to combat noises, their demonstrations are prevented by the stringent experimental requirements, such as perfect quantum operations and the orthogonal condition between the sensing interaction Hamiltonian and the noise Lindbladians. Here, we report an experimental demonstration of a quantum enhancement in sensing with a bosonic probe with different encodings, by exploring the large Hilbert space of the bosonic mode and developing both the approximate QEC and the quantum jump tracking approaches. In a practical radiometry scenario, we attain a 5.3 dB enhancement of sensitivity, which reaches $9.1times10^{-4},mathrm{Hz}^{-1/2}$ when measuring the excitation population of a receiver mode. Our results demonstrate the potential of quantum sensing with near-term quantum technologies, not only shedding new light on the quantum advantage of sensing by revealing its difference from other quantum applications, but also stimulating further efforts on bosonic quantum technologies.