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1. Qualitative inverse problems: mapping data to the features of trajectories and parameter values of an ODE model

   https://doi.org/10.1088/1361-6420/acd414  

   Duan, X.; Rubin, J. E.; Swigon, D. (May 2023, Inverse Problems)

   Abstract Our recent work on linear and affine dynamical systems has laid out a general framework for inferring the parameters of a differential equation model from a discrete set of data points collected from a system being modeled. It introduced a new class of inverse problems where qualitative information about the parameters and the associated dynamics of the system is determined for regions of the data space, rather than just for isolated experiments. Rigorous mathematical results have justified this approach and have identified common features that arise for certain classes of integrable models. In this work we present a thorough numerical investigation that shows that several of these core features extend to a paradigmatic linear-in-parameters model, the Lotka–Volterra (LV) system, which we consider in the conservative case as well as under the addition of terms that perturb the system away from this regime. A central construct for this analysis is a concise representation of parameter and dynamical features in the data space that we call theP_n-diagram, which is particularly useful for visualization of the qualitative dependence of the system dynamics on data for low-dimensional (smalln) systems. Our work also exposes some new properties related to non-uniqueness that arise for these LV systems, with non-uniqueness manifesting as a multi-layered structure in the associatedP₂-diagrams. 

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2. Light Curves and Event Rates of Axion Instability Supernovae

   https://doi.org/10.3847/1538-4357/acaaff  

   Mori, Kanji; Moriya, Takashi J.; Takiwaki, Tomoya; Kotake, Kei; Horiuchi, Shunsaku; Blinnikov, Sergei I. (January 2023, The Astrophysical Journal)

   Abstract It was recently proposed that exotic particles can trigger a new stellar instability that is analogous to thee⁻e⁺pair instability if they are produced and reach equilibrium in the stellar plasma. In this study, we construct axion instability supernova (AISN) models caused by the new instability to predict their observational signatures. We focus on heavy axion-like particles (ALPs) with masses of ∼400 keV–2 MeV and coupling with photons ofg_aγ∼ 10⁻⁵GeV⁻¹. It is found that the⁵⁶Ni mass and the explosion energy are significantly increased by ALPs for a fixed stellar mass. As a result, the peak times of the light curves of AISNe occur earlier than those of standard pair-instability supernovae by 10–20 days when the ALP mass is equal to the electron mass. Also, the event rate of AISNe is 1.7–2.6 times higher than that of pair-instability supernovae, depending on the high mass cutoff of the initial mass function. 

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3. JAXVacua — a framework for sampling string vacua

   https://doi.org/10.1007/JHEP12(2023)146  

   Dubey, A; Krippendorf, S; Schachner, A (December 2023, Journal of High Energy Physics)

   A<sc>bstract</sc> Moduli stabilisation in string compactifications with many light scalars remains a major blind-spot in the string landscape. In these regimes, analytic methods cease to work for generic choices of UV parameters which is why numerical techniques have to be exploited. In this paper, we implement algorithms based on JAX, heavily utilising automatic differentiation, just-in-time compilation and parallelisation features, to efficiently construct string vacua. This implementation provides a golden opportunity to efficiently analyse large unexplored regions of the string landscape. As a first example, we apply our techniques to the search of Type IIB flux vacua in Calabi-Yau orientifold compactifications. We argue that our methods only scale mildly with the Hodge numbers making exhaustive studies of low energy effective field theories with$$ \mathcal{O} $$ $O$(100) scalar fields feasible. Using small computing resources, we are able to construct$$ \mathcal{O} $$ $O$(10⁶) flux vacua per geometry withh^1,2≥ 2, vastly out-performing previous systematic searches. In particular, we showcase the efficiency of our methods by presenting generic vacua with fluxes below the tadpole constraint set by the orientifold with up toh^1,2= 25 complex structure moduli. 

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4. Four‐Selective Pyridine Alkylation via Wittig Olefination of Dearomatized Pyridylphosphonium Ylides

   https://doi.org/10.1002/anie.202109271  

   Fricke, Patrick_J; Dolewski, Ryan_D; McNally, Andrew (August 2021, Angewandte Chemie International Edition)

   Abstract Methods to synthesize alkylated pyridines are valuable because these structures are prevalent in pharmaceuticals and agrochemicals. We have developed a distinct approach to construct 4‐alkylpyridines using dearomatized pyridylphosphonium ylide intermediates in a Wittig olefination‐rearomatization sequence. PyridineN‐activation is key to this strategy, andN‐triazinylpyridinium salts enable coupling between a wide variety of substituted pyridines and aldehydes. The alkylation protocol is viable for late‐stage functionalization, including methylation of pyridine‐containing drugs. This approach represents an alternative to metal‐catalyzedsp²‐sp³cross‐coupling reactions and Minisci‐type processes. 

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5. DeepMinimizer: A Differentiable Framework for Optimizing Sequence-Specific Minimizer Schemes

   https://doi.org/10.1007/978-3-031-04749-7_4  

   Hoang, M.; Zheng, H.; Kingsford, C. (January 2022, RECOMB 2022: Research in Computational Molecular Biology)

   Pe'er, I. (Ed.)

   Minimizers are k-mer sampling schemes designed to generate sketches for large sequences that preserve sufficiently long matches between sequences. Despite their widespread application, learning an effective minimizer scheme with optimal sketch size is still an open question. Most work in this direction focuses on designing schemes that work well on expectation over random sequences, which have limited applicability to many practical tools. On the other hand, several methods have been proposed to construct minimizer schemes for a specific target sequence. These methods, however, require greedy approximations to solve an intractable discrete optimization problem on the permutation space of k-mer orderings. To address this challenge, we propose: (a) a reformulation of the combinatorial solution space using a deep neural network re-parameterization; and (b) a fully differentiable approximation of the discrete objective. We demonstrate that our framework, DEEPMINIMIZER, discovers minimizer schemes that significantly outperform state-of-the-art constructions on genomic sequences. 

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