SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimizing the drag force exerted on an object in a fluid flow. However, these algorithms require an abundance of training examples and may become prohibitively expensive for many applications. In addition, the reliance on deep neural networks often results in an uninterpretable, black-box policy that may be too computationally expensive to use with certain embedded systems. Recent advances in sparse dictionary learning, such as the sparse identification of nonlinear dynamics (SINDy), have shown promise for creating efficient and interpretable data-driven models in the low-data regime. In this work we introduce SINDy-RL, a unifying framework for combining SINDy and DRL to create efficient, interpretable, and trustworthy representations of the dynamics model, reward function, and control policy. We demonstrate the effectiveness of our approaches on benchmark control environments and challenging fluids problems. SINDy-RL achieves comparable performance to state-of-the-art DRL algorithms using significantly fewer interactions in the environment and results in an interpretable control policy orders of magnitude smaller than a deep neural network policy.Comment: 24 pages + 14 appendices (45 pages total). 25 figures, 7 tables. For code, see https://github.com/nzolman/sindy-r

The Universe at Extreme Scale: Multi-Petaflop Sky Simulation on the BG/Q

Remarkable observational advances have established a compelling cross-validated model of the Universe. Yet, two key pillars of this model -- dark matter and dark energy -- remain mysterious. Sky surveys that map billions of galaxies to explore the `Dark Universe', demand a corresponding extreme-scale simulation capability; the HACC (Hybrid/Hardware Accelerated Cosmology Code) framework has been designed to deliver this level of performance now, and into the future. With its novel algorithmic structure, HACC allows flexible tuning across diverse architectures, including accelerated and multi-core systems. On the IBM BG/Q, HACC attains unprecedented scalable performance -- currently 13.94 PFlops at 69.2% of peak and 90% parallel efficiency on 1,572,864 cores with an equal number of MPI ranks, and a concurrency of 6.3 million. This level of performance was achieved at extreme problem sizes, including a benchmark run with more than 3.6 trillion particles, significantly larger than any cosmological simulation yet performed.Comment: 11 pages, 11 figures, final version of paper for talk presented at SC1

Leishmaniavirus-dependent metastatic leishmaniasis is prevented by blocking IL-17A

Cutaneous leishmaniasis has various outcomes, ranging from self-healing reddened papules to extensive open ulcerations that metastasise to secondary sites and are often resistant to standard therapies. In the case of L. guyanensis (L.g), about 5-10% of all infections result in metastatic complications. We recently showed that a cytoplasmic virus within L.g parasites (LRV1) is able to act as a potent innate immunogen, worsening disease outcome in a murine model. In this study, we investigated the immunophenotype of human patients infected by L.g and found a significant association between the inflammatory cytokine IL-17A, the presence of LRV1 and disease chronicity. Further, IL-17A was inversely correlated to the protective cytokine IFN-γ. These findings were experimentally corroborated in our murine model, where IL-17A produced in LRV1+ L.g infection contributed to parasite virulence and dissemination in the absence of IFN-γ. Additionally, IL-17A inhibition in mice using digoxin or SR1001, showed therapeutic promise in limiting parasite virulence. Thus, this murine model of LRV1-dependent infectious metastasis validated markers of disease chronicity in humans and elucidated the immunologic mechanism for the dissemination of Leishmania parasites to secondary sites. Moreover, it confirms the prognostic value of LRV1 and IL-17A detection to prevent metastatic leishmaniasis in human patients

Genetic Drivers of Kidney Defects in the DiGeorge Syndrome

Background The DiGeorge syndrome, the most common of the microdeletion syndromes, affects multiple organs, including the heart, the nervous system, and the kidney. It is caused by deletions on chromosome 22q11.2; the genetic driver of the kidney defects is unknown. Methods We conducted a genomewide search for structural variants in two cohorts: 2080 patients with congenital kidney and urinary tract anomalies and 22,094 controls. We performed exome and targeted resequencing in samples obtained from 586 additional patients with congenital kidney anomalies. We also carried out functional studies using zebrafish and mice. Results We identified heterozygous deletions of 22q11.2 in 1.1% of the patients with congenital kidney anomalies and in 0.01% of population controls (odds ratio, 81.5; P=4.5×10(-14)). We localized the main drivers of renal disease in the DiGeorge syndrome to a 370-kb region containing nine genes. In zebrafish embryos, an induced loss of function in snap29, aifm3, and crkl resulted in renal defects; the loss of crkl alone was sufficient to induce defects. Five of 586 patients with congenital urinary anomalies had newly identified, heterozygous protein-altering variants, including a premature termination codon, in CRKL. The inactivation of Crkl in the mouse model induced developmental defects similar to those observed in patients with congenital urinary anomalies. Conclusions We identified a recurrent 370-kb deletion at the 22q11.2 locus as a driver of kidney defects in the DiGeorge syndrome and in sporadic congenital kidney and urinary tract anomalies. Of the nine genes at this locus, SNAP29, AIFM3, and CRKL appear to be critical to the phenotype, with haploinsufficiency of CRKL emerging as the main genetic driver. (Funded by the National Institutes of Health and others.)

Electronic Structure of Atomically Precise Graphene Nanoribbons

Some of the most intriguing properties of graphene are predicted for specifically designed nanostructures such as nanoribbons. Functionalities far beyond those known from extended graphene systems include electronic band gap variations related to quantum confinement and edge effects, as well as localized spin-polarized edge states for specific edge geometries. The inability to produce graphene nanostructures with the needed precision, however, has so far hampered the verification of the predicted electronic properties. Here, we report on the electronic band gap anddispersion of the occupied electronic bands of atomically precise graphene nanoribbons fabricated via on-surface synthesis. Angle-resolved photoelectron spectroscopy and scanning tunnelingspectroscopy data from armchair graphene nanoribbons of width N = 7 supported on Au(111) reveal a band gap of 2.3 eV, an effective mass of 0.21 m0 at the top of the valence band, and anenergy-dependent charge carrier velocity reaching 8.2 105 m/s in the linear part of the valence band. These results are in quantitative agreement with theoretical predictions that include image charge corrections accounting for screening by the metal substrate and confirm the importance of electron-electron interactions in graphene nanoribbon

Electronic Structure of Atomically Precise Graphene Nanoribbons

Some of the most intriguing properties of graphene are predicted for specifically designed nanostructures such as nanoribbons. Functionalities far beyond those known from extended graphene systems include electronic band gap variations related to quantum confinement and edge effects, as well as localized spin-polarized edge states for specific edge geometries. The inability to produce graphene nanostructures with the needed precision, however, has so far hampered the verification of the predicted electronic properties. Here, we report on the electronic band gap and dispersion of the occupied electronic bands of atomically precise graphene nanoribbons fabricated <i>via</i> on-surface synthesis. Angle-resolved photoelectron spectroscopy and scanning tunneling spectroscopy data from armchair graphene nanoribbons of width <i>N</i> = 7 supported on Au(111) reveal a band gap of 2.3 eV, an effective mass of 0.21 <i>m</i>₀ at the top of the valence band, and an energy-dependent charge carrier velocity reaching 8.2 × 10⁵ m/s in the linear part of the valence band. These results are in quantitative agreement with theoretical predictions that include image charge corrections accounting for screening by the metal substrate and confirm the importance of electron–electron interactions in graphene nanoribbons

On-Surface Synthesis and Characterization of 9‑Atom Wide Armchair Graphene Nanoribbons

The bottom-up approach to synthesize graphene nanoribbons strives not only to introduce a band gap into the electronic structure of graphene but also to accurately tune its value by designing both the width and edge structure of the ribbons with atomic precision. We report the synthesis of an armchair graphene nanoribbon with a width of nine carbon atoms on Au(111) through surface-assisted aryl–aryl coupling and subsequent cyclodehydrogenation of a properly chosen molecular precursor. By combining high-resolution atomic force microscopy, scanning tunneling microscopy, and Raman spectroscopy, we demonstrate that the atomic structure of the fabricated ribbons is exactly as designed. Angle-resolved photoemission spectroscopy and Fourier-transformed scanning tunneling spectroscopy reveal an electronic band gap of 1.4 eV and effective masses of ≈0.1 <i>m</i>_e for both electrons and holes, constituting a substantial improvement over previous efforts toward the development of transistor applications. We use <i>ab initio</i> calculations to gain insight into the dependence of the Raman spectra on excitation wavelength as well as to rationalize the symmetry-dependent contribution of the ribbons’ electronic states to the tunneling current. We propose a simple rule for the visibility of frontier electronic bands of armchair graphene nanoribbons in scanning tunneling spectroscopy