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H₂S/CH₄and CO₂/CH₄separations show opposing trends, making simultaneous improvement challenging. This is addressed by increasing free volume to enhance competitive sorption effects and boosting diffusion selectivity throughin situcrosslinking. 

While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexpected situations. This study investigates model sensitivity to domain shifts, such as data sampled from different hospitals or confounded by demographic variables like sex and race, focusing on chest X-rays and skin lesion images. The key finding is that existing visual backbones lack an appropriate prior for reliable generalization in these settings. Inspired by medical training, the authors propose incorporating explicit medical knowledge communicated in natural language into deep networks. They introduce Knowledge-enhanced Bottlenecks (KnoBo), a class of concept bottleneck models that integrate knowledge priors, enabling reasoning with clinically relevant factors found in medical textbooks or PubMed. KnoBo utilizes retrieval-augmented language models to design an appropriate concept space, paired with an automatic training procedure for recognizing these concepts. Evaluations across 20 datasets demonstrate that KnoBo outperforms fine-tuned models on confounded datasets by 32.4% on average. Additionally, PubMed is identified as a promising resource for enhancing model robustness to domain shifts, outperforming other resources in both information diversity and prediction performance. 

Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models correlate the target properties for NA-MD, implemented with DFT and CPA, directly to the system structure. Trained on small systems, the neural networks are applied to large systems and long timescales, extending NA-MD capabilities by orders of magnitude. We demonstrate the approach with dependence of charge trapping and recombination on defect concentration in MoS₂. Defects provide the main mechanism of charge losses, resulting in performance degradation. Charge trapping slows with decreasing defect concentration; however, recombination exhibits complex dependence, conditional on whether it occurs between free or trapped charges, and relative concentrations of carriers and defects. Delocalized shallow traps can become localized with increasing temperature, changing trapping and recombination behavior. Completely based on ML, the approach bridges the gap between theoretical models and realistic experimental conditions and enables NA-MD on thousand-atom systems and many nanoseconds. 

Every mammal studied to date has been found to have a male mutation bias: male parents transmit more de novo mutations to offspring than female parents, contributing increasingly more mutations with age. Although male-biased mutation has been studied for more than 75 years, its causes are still debated. One obstacle to understanding this pattern is its near universality—without variation in mutation bias, it is difficult to find an underlying cause. Here, we present new data on multiple pedigrees from two primate species: aye-ayes (Daubentonia madagascariensis), a member of the strepsirrhine primates, and olive baboons (Papio anubis). In stark contrast to the pattern found across mammals, we find a much larger effect of maternal age than paternal age on mutation rates in the aye-aye. In addition, older aye-aye mothers transmit substantially more mutations than older fathers. We carry out both computational and experimental validation of our results, contrasting them with results from baboons and other primates using the same methodologies. Further, we analyze a set of DNA repair and replication genes to identify candidate mutations that may be responsible for the change in mutation bias observed in aye-ayes. Our results demonstrate that mutation bias is not an immutable trait, but rather one that can evolve between closely related species. Further work on aye-ayes (and possibly other lemuriform primates) should help to explain the molecular basis for sex-biased mutation. 

Gridless direction-of-arrival (DOA) estimation with multiple frequencies can be applied to acoustic source localization. We formulate this as an atomic norm minimization (ANM) problem and derive a regularization-free semi-definite program (SDP) avoiding regularization bias. We also propose a fast SDP program to deal with non-uniform frequency spacing. The DOA is retrieved via irregular Vandermonde decomposition (IVD), and we theoretically guarantee the existence of the IVD. We extend ANM to the multiple measurement vector setting and derive its equivalent regularization-free SDP. For a uniform linear array using multiple frequencies, we can resolve more sources than the sensors. The effectiveness of the proposed framework is demonstrated via numerical experiments.